{"entity": "journal", "iuid": "20e9cf2850cd432f864595e15b54882a", "timestamp": "2026-09-12T07:55:34.279Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/J%20Chem%20Inf%20Model.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/J%20Chem%20Inf%20Model"}}, "title": "J Chem Inf Model", "issn": "1549-960X", "issn-l": "1549-9596", "publications_count": 31, "publications": [{"entity": "publication", "iuid": "0b4eb772bc0e488aaed040d36844ce84", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/0b4eb772bc0e488aaed040d36844ce84.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/0b4eb772bc0e488aaed040d36844ce84"}}, "title": "Multidimensional Decomposition and Ensemble Modeling of Histatin 1 and Its Siblings: Detailing Structure and Biological Function Using an Integrative Approach.", "authors": [{"family": "Svensson", "given": "Oskar", "initials": "O", "orcid": "0000-0003-0961-0078", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/abf460c86ae24da78412e07597b30f4e.json"}}, {"family": "Gerelli", "given": "Yuri", "initials": "Y", "orcid": "0000-0001-5655-8298", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/90e9b15d2ddc4d27a9c13e28b897f550.json"}}, {"family": "Skep\u00f6", "given": "Marie", "initials": "M", "orcid": "0000-0002-8639-9993", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e11a9f793a304aa481751f1ae0abe62c.json"}}], "type": "journal article", "published": "2025-07-14", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "65", "issue": "13", "pages": "7089-7101", "issn-l": "1549-9596"}, "abstract": "Histatins are a family of multifunctional, cationic histidine-rich saliva peptides. The most prominently represented are Histatin 1, Histatin 3, and Histatin 5. Despite considerable similarities in primary structure, the three members are known to display varied antimicrobial properties and healing abilities. This study aims to provide a detailed structural comparison of Histatin 1, Histatin 3, and Histatin 5, as well as a thorough investigation into the variation caused to the conformational ensemble of Histatin 1 upon phosphorylation. The study applies molecular dynamics simulation, small-angle X-ray scattering, circular dichroism, bioinformatics tools, and neutron reflectometry. A multidimensional decomposition technique and its connection to clustering methods are also presented. It was observed that the phosphorylation of Histatin 1 profoundly shifts the conformational ensemble and may act as a molecular switch that facilitates tooth enamel binding. Observations are provided on the killing mechanisms of Histatins concerning self-association and membrane rupturing.", "doi": "10.1021/acs.jcim.5c00854", "pmid": "40600658", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12264947"}], "notes": [], "created": "2026-08-20T08:10:34.696Z", "modified": "2026-08-20T08:10:34.856Z"}, {"entity": "publication", "iuid": "7737851a912241f4960b61fa42bdb87f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/7737851a912241f4960b61fa42bdb87f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/7737851a912241f4960b61fa42bdb87f"}}, "title": "Quantifying Acyl Chain Interdigitation in Simulated Bilayers via Direct Transbilayer Interactions.", "authors": [{"family": "Chaisson", "given": "Emily H", "initials": "EH", "orcid": "0009-0002-7876-9736", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/47f6b458684f4496933d3ebeb32f0aab.json"}}, {"family": "Heberle", "given": "Frederick A", "initials": "FA", "orcid": "0000-0002-0424-3240", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2557f5621a0b4c87b5f4702fbc7b6bdb.json"}}, {"family": "Doktorova", "given": "Milka", "initials": "M", "orcid": "0000-0003-4366-2242", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/791384c319f04584bac8ffb21df7271f.json"}}], "type": "journal article", "published": "2025-04-28", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "65", "issue": "8", "pages": "3879-3885", "issn-l": "1549-9596"}, "abstract": "In a lipid bilayer, the interactions between the lipid hydrocarbon chains from opposing leaflets can influence membrane properties. These interactions include the phenomenon of interdigitation, in which an acyl chain of one leaflet extends past the bilayer midplane and into the opposing leaflet. While static interdigitation is well understood in gel-phase bilayers from X-ray diffraction measurements, much less is known about dynamic interdigitation in fluid phases. In this regard, atomistic molecular dynamics simulations can provide mechanistic information on interleaflet interactions that can be used to generate experimentally testable hypotheses. To address limitations of existing computational methodologies that provide results that are either indirect or averaged over time and space, here we introduce three novel ways of quantifying the extent of chain interdigitation. Our protocols include the analysis of instantaneous interactions at the level of individual carbon atoms, thus providing temporal and spatial resolution for a more nuanced picture of dynamic interdigitation. We compare the methods on bilayers composed of lipids with an equal total number of carbon atoms, but different mismatches between the sn-1 and sn-2 chain lengths. We find that these metrics, which are based on freely available software packages and are easy to implement, provide complementary details that help characterize various features of lipid-lipid contacts at the bilayer midplane. The new frameworks thus allow for a deeper look at fundamental molecular mechanisms underlying bilayer structure and dynamics and present a valuable expansion of the membrane biophysics toolkit.", "doi": "10.1021/acs.jcim.4c02287", "pmid": "40237313", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12042268"}], "notes": [], "created": "2026-08-20T08:10:32.619Z", "modified": "2026-08-20T08:10:32.734Z"}, {"entity": "publication", "iuid": "8ab7cf0deb95460c8a9af12e9b7d9e7f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/8ab7cf0deb95460c8a9af12e9b7d9e7f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/8ab7cf0deb95460c8a9af12e9b7d9e7f"}}, "title": "Secondary Binding Site of CYP17A1 in Enhanced Sampling Simulations.", "authors": [{"family": "Wr\u00f3bel", "given": "Tomasz M", "initials": "TM", "orcid": "0000-0002-0313-2522", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/63e0ecf2f00c462fad21cd18aa24b3cd.json"}}, {"family": "Bartuzi", "given": "Damian", "initials": "D"}, {"family": "Kaczor", "given": "Agnieszka A", "initials": "AA", "orcid": "0000-0001-8679-9623", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b12c9906344e49e99ded4746e8204544.json"}}], "type": "journal article", "published": "2024-10-14", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "64", "issue": "19", "pages": "7679-7686", "issn-l": "1549-9596"}, "abstract": "Androgens like testosterone and dihydrotestosterone play a key role in prostate cancer progression, making the enzyme CYP17A1, essential for androgen synthesis, a crucial therapeutic target. Recent studies have revealed electron density at the substrate entry channel, suggesting the presence of a secondary binding site. In this study, we calculated the binding free energy landscape of known ligands at this site using Funnel Metadynamics. Our results characterize this binding site and indicate that nonheme-interacting ligands could effectively bind to CYP17A1, providing a novel approach to the design of CYP17A1 inhibitors.", "doi": "10.1021/acs.jcim.4c01293", "pmid": "39325660", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11480979"}], "notes": [], "created": "2026-08-21T11:33:05.011Z", "modified": "2026-08-21T11:33:05.096Z"}, {"entity": "publication", "iuid": "fe8d85db6e044230a5b352a9fe150e8b", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/fe8d85db6e044230a5b352a9fe150e8b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/fe8d85db6e044230a5b352a9fe150e8b"}}, "title": "Combining Data-Driven and Structure-Based Approaches in Designing Dual PARP1-BRD4 Inhibitors for Breast Cancer Treatment.", "authors": [{"family": "Feng", "given": "Bo", "initials": "B", "orcid": "0000-0002-5562-3358", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4d310dda32f4bbc87c0f57e0d017bc0.json"}}, {"family": "Yu", "given": "Hui", "initials": "H"}, {"family": "Dong", "given": "Xu", "initials": "X"}, {"family": "D\u00edaz-Holgu\u00edn", "given": "Alejandro", "initials": "A"}, {"family": "Antolin", "given": "Albert A", "initials": "AA"}, {"family": "Hu", "given": "Huabin", "initials": "H", "orcid": "0009-0001-6851-6340", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bad19cdfcd04e6db5f2650f7d9cd750.json"}}], "type": "journal article", "published": "2024-10-14", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "64", "issue": "19", "pages": "7725-7742", "issn-l": "1549-9596"}, "abstract": "Poly(ADP-ribose) polymerase 1 (PARP1) inhibitors have revolutionized the treatment of many cancers with DNA-repairing deficiencies via synthetic lethality. Advocated by the polypharmacology concept, recent evidence discovered that a significantly synergistic effect in increasing the death of cancer cells was observed by simultaneously perturbating the enzymatic activities of bromodomain-containing protein 4 (BRD4) and PARP1. Here, we developed a novel cheminformatics approach combined with a structure-based method aiming to facilitate the design of dual PARP1-BRD4 inhibitors. Instead of linking pharmacophores, the developed approach first identified merged pharmacophores (a pool of amide-containing ring systems), from which phenanthridin-6(5H)-one was further prioritized. Based on this starting point, several small molecules were rationally designed, among which HF4 exhibited low micromolar inhibitory activity against BRD4 and PARP1, particularly exhibiting strong inhibition of BRD4 BD1 with an IC50 value of 204 nM. Furthermore, it demonstrated potent antiproliferative effects against breast cancer gene-deficient and proficient breast cancer cell lines by arresting cell cycle progression and impeding DNA damage repair. Collectively, our systematic efforts to design lead-like molecules have the potential to open doors for the exploration of dual PARP1-BRD4 inhibitors as a promising avenue for breast cancer treatment. Furthermore, the developed approach can be extended to systematically design inhibitors targeting PARP1 and other related targets.", "doi": "10.1021/acs.jcim.4c01421", "pmid": "39292752", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11480993"}], "notes": [], "created": "2026-08-21T11:33:07.122Z", "modified": "2026-08-21T11:33:07.228Z"}, {"entity": "publication", "iuid": "e442230f6db64c3e8e8a81d3a19ef3eb", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/e442230f6db64c3e8e8a81d3a19ef3eb.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/e442230f6db64c3e8e8a81d3a19ef3eb"}}, "title": "Simulating the Skin Permeation Process of Ionizable Molecules.", "authors": [{"family": "Lundborg", "given": "Magnus", "initials": "M", "orcid": "0000-0002-0873-7854", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ab1b1b7c73fd44b9b880106feaa56feb.json"}}, {"family": "Wennberg", "given": "Christian", "initials": "C", "orcid": "0000-0003-4012-1678", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4a7c95013dd467591e4e9603cc7bfad.json"}}, {"family": "Lindahl", "given": "Erik", "initials": "E", "orcid": "0000-0002-2734-2794", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9eb30fa60f9b4b95842ac9d9f3a0eaa9.json"}}, {"family": "Norl\u00e9n", "given": "Lars", "initials": "L", "orcid": "0000-0002-8049-2556", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b1a3490a42364d7b9925fcfae614d33f.json"}}], "type": "journal article", "published": "2024-07-08", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "64", "issue": "13", "pages": "5295-5302", "issn-l": "1549-9596"}, "abstract": "It is commonly assumed that ionizable molecules, such as drugs, permeate through the skin barrier in their neutral form. By using molecular dynamics simulations of the charged and neutral states separately, we can study the dynamic protonation behavior during the permeation process. We have studied three weak acids and three weak bases and conclude that the acids are ionized to a larger extent than the bases, when passing through the headgroup region of the lipid barrier structure, at pH values close to their pKa. It can also be observed that even if these dynamic protonation simulations are informative, in the cases studied herein they are not necessary for the calculation of permeability coefficients. It is sufficient to base the calculations only on the neutral form, as is commonly done.", "doi": "10.1021/acs.jcim.4c00722", "pmid": "38917349", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11234375"}], "notes": [], "created": "2026-08-20T08:10:30.487Z", "modified": "2026-08-20T08:10:30.583Z"}, {"entity": "publication", "iuid": "eeaa6e472c3a4ff88c013a1d34b4e740", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/eeaa6e472c3a4ff88c013a1d34b4e740.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/eeaa6e472c3a4ff88c013a1d34b4e740"}}, "title": "Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank.", "authors": [{"family": "Seal", "given": "Srijit", "initials": "S", "orcid": "0000-0003-2790-8679", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf360510777949acaa72a940f13e17bf.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O"}, {"family": "Hosseini-Gerami", "given": "Layla", "initials": "L"}, {"family": "Garc\u00eda-Orteg\u00f3n", "given": "Miguel", "initials": "M", "orcid": "0000-0003-4372-4706", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/edbfcd5c5cd64e06adffa372ab46519f.json"}}, {"family": "Singh", "given": "Shantanu", "initials": "S"}, {"family": "Bender", "given": "Andreas", "initials": "A", "orcid": "0000-0002-6683-7546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fea25f8013a14bb587c85df858f74d55.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}], "type": "journal article", "published": "2024-02-26", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "64", "issue": "4", "pages": "1172-1186", "issn-l": "1549-9596"}, "abstract": "Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.", "doi": "10.1021/acs.jcim.3c01834", "pmid": "38300851", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10900289"}, {"db": "figshare", "key": "10.6084/m9.figshare.24312274"}], "notes": [], "created": "2026-08-20T08:10:28.376Z", "modified": "2026-08-20T08:10:28.514Z"}, {"entity": "publication", "iuid": "3b912b2e151e41f791e16a35b7ac4dd5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/3b912b2e151e41f791e16a35b7ac4dd5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/3b912b2e151e41f791e16a35b7ac4dd5"}}, "title": "phbuilder: A Tool for Efficiently Setting up Constant pH Molecular Dynamics Simulations in GROMACS.", "authors": [{"family": "Jansen", "given": "Anton", "initials": "A", "orcid": "0000-0003-2835-7987", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ad554e8805364a86b5214220e7731088.json"}}, {"family": "Aho", "given": "Noora", "initials": "N", "orcid": "0009-0008-1243-0215", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b91ecb55f5794b1a836a064c89796b50.json"}}, {"family": "Groenhof", "given": "Gerrit", "initials": "G", "orcid": "0000-0001-8148-5334", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c6b4d7e99ff644b1a39e18533528b975.json"}}, {"family": "Buslaev", "given": "Pavel", "initials": "P", "orcid": "0000-0003-2031-4691", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c0cd04388c6e49d88506e676091e78df.json"}}, {"family": "Hess", "given": "Berk", "initials": "B"}], "type": "journal article", "published": "2024-02-12", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "64", "issue": "3", "pages": "567-574", "issn-l": "1549-9596"}, "abstract": "Constant pH molecular dynamics (MD) is a powerful technique that allows the protonation state of residues to change dynamically, thereby enabling the study of pH dependence in a manner that has not been possible before. Recently, a constant pH implementation was incorporated into the GROMACS MD package. Although this implementation provides good accuracy and performance, manual modification and the preparation of simulation input files are required, which can be complicated, tedious, and prone to errors. To simplify and automate the setup process, we present phbuilder, a tool that automatically prepares constant pH MD simulations for GROMACS by modifying the input structure and topology as well as generating the necessary parameter files. phbuilder can prepare constant pH simulations from both initial structures and existing simulation systems, and it also provides functionality for performing titrations and single-site parametrizations of new titratable group types. The tool is freely available at www.gitlab.com/gromacs-constantph. We anticipate that phbuilder will make constant pH simulations easier to set up, thereby making them more accessible to the GROMACS user community.", "doi": "10.1021/acs.jcim.3c01313", "pmid": "38215282", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10865341"}], "notes": [], "created": "2026-08-20T08:10:26.014Z", "modified": "2026-08-20T08:10:26.226Z"}, {"entity": "publication", "iuid": "f66d5fe9bc2a4a0b95909cf2ea4a533c", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f66d5fe9bc2a4a0b95909cf2ea4a533c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f66d5fe9bc2a4a0b95909cf2ea4a533c"}}, "title": "PSW-Designer: An Open-Source Computational Platform for the Design and Virtual Screening of Photopharmacological Ligands.", "authors": [{"family": "Simon", "given": "Icaro A", "initials": "IA", "orcid": "0000-0003-4550-4248", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fb44e582add64ffa97005d32ac68b83a.json"}}, {"family": "Homan", "given": "Evert J", "initials": "EJ"}, {"family": "Wijtmans", "given": "Maikel", "initials": "M", "orcid": "0000-0001-8955-8016", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9505bd281e6842efaf25bc7fb3e7ad66.json"}}, {"family": "Sundstr\u00f6m", "given": "Michael", "initials": "M"}, {"family": "Leurs", "given": "Rob", "initials": "R", "orcid": "0000-0003-1354-2848", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/43ccb10fb9e64f9d998be5d03556b2ed.json"}}, {"family": "De Esch", "given": "Iwan J P", "initials": "IJP", "orcid": "0000-0002-1969-0238", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4a99c2dd4b4c416ba1c38be113bed2a9.json"}}, {"family": "Zarzycka", "given": "Barbara A", "initials": "BA", "orcid": "0000-0002-7202-5317", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/72922bb03947482aab23bb612406a0b7.json"}}], "type": "journal article", "published": "2023-11-13", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "63", "issue": "21", "pages": "6696-6705", "issn-l": "1549-9596"}, "abstract": "Photoswitchable (PSW) molecules offer an attractive opportunity for the optical control of biological processes. However, the successful design of such compounds remains a challenging multioptimization endeavor, resulting in several biological target classes still relatively poorly explored by photoswitchable ligands, as is the case for G protein-coupled receptors (GPCRs). Here, we present the PSW-Designer, a fully open-source computational platform, implemented in the KNIME Analytics Platform, to design and virtually screen novel photoswitchable ligands for photopharmacological applications based on privileged scaffolds. We demonstrate the applicability of the PSW-Designer to GPCRs and assess its predictive capabilities via two retrospective case studies. Furthermore, by leveraging bioactivity information on known ligands, typical and atypical strategies for photoswitchable group incorporation, and the increasingly structural information available for biological targets, the PSW-Design will facilitate the design of novel photoswitchable molecules with improved photopharmacological properties and increased binding affinity shifts upon illumination for GPCRs and many other protein targets.", "doi": "10.1021/acs.jcim.3c01050", "pmid": "37831965", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10647043"}], "notes": [], "created": "2026-08-21T11:33:02.515Z", "modified": "2026-08-21T11:33:02.645Z"}, {"entity": "publication", "iuid": "5e745725d48c4b14b3fd095607998d4f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5e745725d48c4b14b3fd095607998d4f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5e745725d48c4b14b3fd095607998d4f"}}, "title": "Understanding Drug Skin Permeation Enhancers Using Molecular Dynamics Simulations.", "authors": [{"family": "Wennberg", "given": "Christian", "initials": "C", "orcid": "0000-0003-4012-1678", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4a7c95013dd467591e4e9603cc7bfad.json"}}, {"family": "Lundborg", "given": "Magnus", "initials": "M", "orcid": "0000-0002-0873-7854", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ab1b1b7c73fd44b9b880106feaa56feb.json"}}, {"family": "Lindahl", "given": "Erik", "initials": "E"}, {"family": "Norl\u00e9n", "given": "Lars", "initials": "L", "orcid": "0000-0002-8049-2556", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b1a3490a42364d7b9925fcfae614d33f.json"}}], "type": "journal article", "published": "2023-08-14", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "63", "issue": "15", "pages": "4900-4911", "issn-l": "1549-9596"}, "abstract": "Our skin constitutes an effective permeability barrier that protects the body from exogenous substances but concomitantly severely limits the number of pharmaceutical drugs that can be delivered transdermally. In topical formulation design, chemical permeation enhancers (PEs) are used to increase drug skin permeability. In vitro skin permeability experiments can measure net effects of PEs on transdermal drug transport, but they cannot explain the molecular mechanisms of interactions between drugs, permeation enhancers, and skin structure, which limits the possibility to rationally design better new drug formulations. Here we investigate the effect of the PEs water, lauric acid, geraniol, stearic acid, thymol, ethanol, oleic acid, and eucalyptol on the transdermal transport of metronidazole, caffeine, and naproxen. We use atomistic molecular dynamics (MD) simulations in combination with developed molecular models to calculate the free energy difference between 11 PE-containing formulations and the skin's barrier structure. We then utilize the results to calculate the final concentration of PEs in skin. We obtain an RMSE of 0.58 log units for calculated partition coefficients from water into the barrier structure. We then use the modified PE-containing barrier structure to calculate the PEs' permeability enhancement ratios (ERs) on transdermal metronidazole, caffeine, and naproxen transport and compare with the results obtained from in vitro experiments. We show that MD simulations are able to reproduce rankings based on ERs. However, strict quantitative correlation with experimental data needs further refinement, which is complicated by significant deviations between different measurements. Finally, we propose a model for how to use calculations of the potential of mean force of drugs across the skin's barrier structure in a topical formulation design.", "doi": "10.1021/acs.jcim.3c00625", "pmid": "37462219", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10428223"}], "notes": [], "created": "2026-08-20T08:10:23.956Z", "modified": "2026-08-20T08:10:24.127Z"}, {"entity": "publication", "iuid": "a8c7fcbc453448719abcb5d35435549d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/a8c7fcbc453448719abcb5d35435549d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/a8c7fcbc453448719abcb5d35435549d"}}, "title": "Simulation Reveals the Chameleonic Behavior of Macrocycles.", "authors": [{"family": "Sethio", "given": "Daniel", "initials": "D", "orcid": "0000-0002-8075-1482", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f9d68c4ef8374fe9b3f56f112d8feb2d.json"}}, {"family": "Poongavanam", "given": "Vasanthanathan", "initials": "V", "orcid": "0000-0002-8880-9247", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/84ad07811ee94bd2a76b9090941cde51.json"}}, {"family": "Xiong", "given": "Ruisheng", "initials": "R"}, {"family": "Tyagi", "given": "Mohit", "initials": "M"}, {"family": "Duy Vo", "given": "Duc", "initials": "D"}, {"family": "Lindh", "given": "Roland", "initials": "R", "orcid": "0000-0001-7567-8295", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e748a7dbd02742dcaeecc722ea446b21.json"}}, {"family": "Kihlberg", "given": "Jan", "initials": "J", "orcid": "0000-0002-4205-6040", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7edb4dfa7c4b4d6c8c4cf0282a9c9a1a.json"}}], "type": "journal article", "published": "2023-01-09", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "63", "issue": "1", "pages": "138-146", "issn-l": "1549-9596"}, "abstract": "Conformational analysis is central to the design of bioactive molecules. It is particularly challenging for macrocycles due to noncovalent transannular interactions, steric interactions, and ring strain that are often coupled. Herein, we simulated the conformations of five macrocycles designed to express a progression of increasing complexity in environment-dependent intramolecular interactions and verified the results against NMR measurements in chloroform and dimethyl sulfoxide. Molecular dynamics using an explicit solvent model, but not the Monte Carlo method with implicit solvation, handled both solvents correctly. Refinement of conformations at the ab initio level was fundamental to reproducing the experimental observations\u2500standard state-of-the-art molecular mechanics force fields were insufficient. Our simulations correctly predicted the intramolecular interactions between side chains and the macrocycle and revealed an unprecedented solvent-induced conformational switch of the macrocyclic ring. Our results provide a platform for the rational, prospective design of molecular chameleons that adapt to the properties of the environment.", "doi": "10.1021/acs.jcim.2c01093", "pmid": "36563083", "labels": {"Ruisheng Xiong": null, "SciLifeLab Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC9832480"}], "notes": [], "created": "2025-11-27T17:34:08.044Z", "modified": "2025-11-27T17:34:08.145Z"}, {"entity": "publication", "iuid": "a297073f06f2493987a445c4d575e966", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/a297073f06f2493987a445c4d575e966.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/a297073f06f2493987a445c4d575e966"}}, "title": "Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.", "authors": [{"family": "Tuerkova", "given": "Alzbeta", "initials": "A"}, {"family": "Bongers", "given": "Brandon J", "initials": "BJ"}, {"family": "Norinder", "given": "Ulf", "initials": "U", "orcid": "0000-0003-3107-331X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da36a1cdadab4126b4e2527721ac2710.json"}}, {"family": "Ungv\u00e1ri", "given": "Orsolya", "initials": "O"}, {"family": "Sz\u00e9kely", "given": "Vir\u00e1g", "initials": "V"}, {"family": "Tarnovskiy", "given": "Andrey", "initials": "A"}, {"family": "Szak\u00e1cs", "given": "Gergely", "initials": "G"}, {"family": "\u00d6zvegy-Laczka", "given": "Csilla", "initials": "C"}, {"family": "van Westen", "given": "Gerard J P", "initials": "GJP", "orcid": "0000-0003-0717-1817", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b487ce42d3ad4be795b97d3d41f95357.json"}}, {"family": "Zdrazil", "given": "Barbara", "initials": "B", "orcid": "0000-0001-9395-1515", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3ac81e512aaf49b984436e937493c700.json"}}], "type": "journal article", "published": "2022-12-26", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "62", "issue": "24", "pages": "6323-6335", "issn-l": "1549-9596"}, "abstract": "Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 \u03bcM), three OATP1B1 inhibitors (2.69 to 10 \u03bcM), and five OATP1B3 inhibitors (1.53 to 10 \u03bcM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.", "doi": "10.1021/acs.jcim.1c01460", "pmid": "35274943", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9795544"}], "notes": [], "created": "2026-08-21T11:33:00.261Z", "modified": "2026-08-21T11:33:00.356Z"}, {"entity": "publication", "iuid": "7858c9014b094f75b3cc6bd6595f5398", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/7858c9014b094f75b3cc6bd6595f5398.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/7858c9014b094f75b3cc6bd6595f5398"}}, "title": "Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA.", "authors": [{"family": "Crean", "given": "Rory M", "initials": "RM"}, {"family": "Pudney", "given": "Christopher R", "initials": "CR", "orcid": "0000-0001-6211-0086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e46df883c623428aba5d01bcf59c1895.json"}}, {"family": "Cole", "given": "David K", "initials": "DK"}, {"family": "van der Kamp", "given": "Marc W", "initials": "MW", "orcid": "0000-0002-8060-3359", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9e09f39776374aeca06c0a767d62bfac.json"}}], "type": "journal article", "published": "2022-02-14", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "62", "issue": "3", "pages": "577-590", "issn-l": "1549-9596"}, "abstract": "Accurate and efficient in silico ranking of protein-protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson-Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (\u22644) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5-10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.", "doi": "10.1021/acs.jcim.1c00765", "pmid": "35049312", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9097153"}], "notes": [], "created": "2026-08-21T11:32:58.073Z", "modified": "2026-08-21T11:32:58.145Z"}, {"entity": "publication", "iuid": "1961b80f32fe4b7285ebe1384a66c93f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1961b80f32fe4b7285ebe1384a66c93f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1961b80f32fe4b7285ebe1384a66c93f"}}, "title": "Allosteric Effect of Nanobody Binding on Ligand-Specific Active States of the \u03b22 Adrenergic Receptor.", "authors": [{"family": "Chen", "given": "Yue", "initials": "Y"}, {"family": "Fleetwood", "given": "Oliver", "initials": "O", "orcid": "0000-0002-4277-2661", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/352d2fedb9d14f2fb11a1c7376a0d878.json"}}, {"family": "P\u00e9rez-Conesa", "given": "Sergio", "initials": "S"}, {"family": "Delemotte", "given": "Lucie", "initials": "L", "orcid": "0000-0002-0828-3899", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/87eaf619d7bd487ebdbe68c46b827e66.json"}}], "type": "journal article", "published": "2021-11-15", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "issn-l": "1549-9596"}, "abstract": "Nanobody binding stabilizes G-protein-coupled receptors (GPCR) in a fully active state and modulates their affinity for bound ligands. However, the atomic-level basis for this allosteric regulation remains elusive. Here, we investigate the conformational changes induced by the binding of a nanobody (Nb80) on the active-like \u03b22 adrenergic receptor (\u03b22AR) via enhanced sampling molecular dynamics simulations. Dimensionality reduction analysis shows that Nb80 stabilizes structural features of the \u03b22AR with an \u223c14 \u00c5 outward movement of transmembrane helix 6 and a close proximity of transmembrane (TM) helices 5 and 7, and favors the fully active-like conformation of the receptor, independent of ligand binding, in contrast to the conditions under which no intracellular binding partner is bound, in which case the receptor is only stabilized in an intermediate-active state. This activation is supported by the residues located at hotspots located on TMs 5, 6, and 7, as shown by supervised machine learning methods. Besides, ligand-specific subtle differences in the conformations assumed by intracellular loop 2 and extracellular loop 2 are captured from the trajectories of various ligand-bound receptors in the presence of Nb80. Dynamic network analysis further reveals that Nb80 binding triggers tighter and stronger local communication networks between the Nb80 and the ligand-binding sites, primarily involving residues around ICL2 and the intracellular end of TM3, TM5, TM6, as well as ECL2, ECL3, and the extracellular ends of TM6 and TM7. In particular, we identify unique allosteric signal transmission mechanisms between the Nb80-binding site and the extracellular domains in conformations modulated by a full agonist, BI167107, and a G-protein-biased partial agonist, salmeterol, involving mainly TM1 and TM2, and TM5, respectively. Altogether, our results provide insights into the effect of intracellular binding partners on the GPCR activation mechanism, which should be taken into account in structure-based drug discovery.", "doi": "10.1021/acs.jcim.1c00826", "pmid": "34780174", "labels": {"Lucie Delemotte": null, "SciLifeLab Fellow": null}, "xrefs": [], "notes": [], "created": "2021-12-08T08:12:29.787Z", "modified": "2022-11-04T11:32:12.319Z"}, {"entity": "publication", "iuid": "1e40baf9dd7a42e3b56fbb2872e72705", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1e40baf9dd7a42e3b56fbb2872e72705.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1e40baf9dd7a42e3b56fbb2872e72705"}}, "title": "Machine Learning Strategies When Transitioning between Biological Assays.", "authors": [{"family": "Arvidsson McShane", "given": "Staffan", "initials": "S", "orcid": "0000-0001-6709-7116", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1529ee0acadb4f76bff770775ebd633b.json"}}, {"family": "Ahlberg", "given": "Ernst", "initials": "E"}, {"family": "Noeske", "given": "Tobias", "initials": "T", "orcid": "0000-0002-0824-6342", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d3177a8180f1425f86e6c086812d71b5.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}], "type": "journal article", "published": "2021-07-26", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "61", "issue": "7", "pages": "3722-3733", "issn-l": "1549-9596"}, "abstract": "Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a retrospective study, we here explore different modeling strategies of how to combine data from the old and new assays when training conformal prediction models using data from hERG and NaV assays. We suggest to continuously monitor the validity and efficiency of models as more data is accumulated from the new assay and select a modeling strategy based on these metrics. In order to maximize the utility of data from the old assay, we propose a strategy that augments the proper training set of an inductive conformal predictor by adding data from the old assay but only having data from the new assay in the calibration set, which results in valid (well-calibrated) models with improved efficiency compared to other strategies. We study the results for varying sizes of new and old assays, allowing for discussion of different practical scenarios. We also conclude that our proposed assay transition strategy is more beneficial, and the value of data from the new assay is higher, for the harder case of regression compared to classification problems.", "doi": "10.1021/acs.jcim.1c00293", "pmid": "34152755", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8317157"}], "notes": [], "created": "2026-08-20T08:10:22.086Z", "modified": "2026-08-20T08:10:22.231Z"}, {"entity": "publication", "iuid": "b3d037415af24095b88dffae65b397ea", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b3d037415af24095b88dffae65b397ea.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b3d037415af24095b88dffae65b397ea"}}, "title": "Data-Driven Ensemble Docking to Map Molecular Interactions of Steroid Analogs with Hepatic Organic Anion Transporting Polypeptides.", "authors": [{"family": "Tuerkova", "given": "Alzbeta", "initials": "A"}, {"family": "Ungv\u00e1ri", "given": "Orsolya", "initials": "O"}, {"family": "Laczk\u00f3-Rig\u00f3", "given": "R\u00e9ka", "initials": "R"}, {"family": "Merny\u00e1k", "given": "Erzs\u00e9bet", "initials": "E"}, {"family": "Szak\u00e1cs", "given": "Gergely", "initials": "G"}, {"family": "\u00d6zvegy-Laczka", "given": "Csilla", "initials": "C"}, {"family": "Zdrazil", "given": "Barbara", "initials": "B", "orcid": "0000-0001-9395-1515", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3ac81e512aaf49b984436e937493c700.json"}}], "type": "journal article", "published": "2021-06-28", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "61", "issue": "6", "pages": "3109-3127", "issn-l": "1549-9596"}, "abstract": "Hepatic organic anion transporting polypeptides-OATP1B1, OATP1B3, and OATP2B1-are expressed at the basolateral membrane of hepatocytes, being responsible for the uptake of a wide range of natural substrates and structurally unrelated pharmaceuticals. Impaired function of hepatic OATPs has been linked to clinically relevant drug-drug interactions leading to altered pharmacokinetics of administered drugs. Therefore, understanding the commonalities and differences across the three transporters represents useful knowledge to guide the drug discovery process at an early stage. Unfortunately, such efforts remain challenging because of the lack of experimentally resolved protein structures for any member of the OATP family. In this study, we established a rigorous computational protocol to generate and validate structural models for hepatic OATPs. The multistep procedure is based on the systematic exploration of available protein structures with shared protein folding using normal-mode analysis, the calculation of multiple template backbones from elastic network models, the utilization of multiple template conformations to generate OATP structural models with various degrees of conformational flexibility, and the prioritization of models on the basis of enrichment docking. We employed the resulting OATP models of OATP1B1, OATP1B3, and OATP2B1 to elucidate binding modes of steroid analogs in the three transporters. Steroid conjugates have been recognized as endogenous substrates of these transporters. Thus, investigating this data set delivers insights into mechanisms of substrate recognition. In silico predictions were complemented with in vitro studies measuring the bioactivity of a compound set on OATP expressing cell lines. Important structural determinants conferring shared and distinct binding patterns of steroid analogs in the three transporters have been identified. Overall, this comparative study provides novel insights into hepatic OATP-ligand interactions and selectivity. Furthermore, the integrative computational workflow for structure-based modeling can be leveraged for other pharmaceutical targets of interest.", "doi": "10.1021/acs.jcim.1c00362", "pmid": "34105971", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8243326"}], "notes": [], "created": "2026-08-21T11:32:55.706Z", "modified": "2026-08-21T11:32:55.757Z"}, {"entity": "publication", "iuid": "e628ae6fbf534ed1a9f8cf5c5fa63ab8", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/e628ae6fbf534ed1a9f8cf5c5fa63ab8.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/e628ae6fbf534ed1a9f8cf5c5fa63ab8"}}, "title": "Toward a Computational Ecotoxicity Assay.", "authors": [{"family": "Kamerlin", "given": "Natasha", "initials": "N", "orcid": "0000-0003-0116-8326", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a9922963283040e3b47e870359d12785.json"}}, {"family": "Delcey", "given": "Micka\u00ebl G", "initials": "MG", "orcid": "0000-0001-9883-3569", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f87c36af206d4ec9881a3e1b8ce8c6ba.json"}}, {"family": "Manzetti", "given": "Sergio", "initials": "S", "orcid": "0000-0003-4240-513X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/224bae255b0b4ff789981502f77210c9.json"}}, {"family": "van der Spoel", "given": "David", "initials": "D", "orcid": "0000-0002-7659-8526", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a5e69bc348284be9936c3f4ac9c6cb35.json"}}], "type": "journal article", "published": "2020-08-24", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "60", "issue": "8", "pages": "3792-3803", "issn-l": "1549-9596"}, "abstract": "Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.", "doi": "10.1021/acs.jcim.0c00574", "pmid": "32648756", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-21T11:32:53.424Z", "modified": "2026-08-21T11:32:53.632Z"}, {"entity": "publication", "iuid": "ce71a0f88896406883baaaa8ed534c6d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ce71a0f88896406883baaaa8ed534c6d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ce71a0f88896406883baaaa8ed534c6d"}}, "title": "Using Predicted Bioactivity Profiles to Improve Predictive Modeling.", "authors": [{"family": "Norinder", "given": "Ulf", "initials": "U", "orcid": "0000-0003-3107-331X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da36a1cdadab4126b4e2527721ac2710.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Svensson", "given": "Fredrik", "initials": "F", "orcid": "0000-0002-5556-8133", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/10109223892d4924bbf4986bf71a7846.json"}}], "type": "journal article", "published": "2020-06-22", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "60", "issue": "6", "pages": "2830-2837", "issn-l": "1549-9596"}, "abstract": "Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.", "doi": "10.1021/acs.jcim.0c00250", "pmid": "32374618", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T08:10:19.854Z", "modified": "2026-08-20T08:10:19.988Z"}, {"entity": "publication", "iuid": "6ffd6dbec4c445ef8cee640326d5b1c2", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/6ffd6dbec4c445ef8cee640326d5b1c2.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/6ffd6dbec4c445ef8cee640326d5b1c2"}}, "title": "Making Soup: Preparing and Validating Models of the Bacterial Cytoplasm for Molecular Simulation.", "authors": [{"family": "Oliveira Bortot", "given": "Leandro", "initials": "L", "orcid": "0000-0002-0915-8205", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/744d2ff5206e45caa18594cdbf2e122e.json"}}, {"family": "Bashardanesh", "given": "Zahedeh", "initials": "Z"}, {"family": "van der Spoel", "given": "David", "initials": "D", "orcid": "0000-0002-7659-8526", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a5e69bc348284be9936c3f4ac9c6cb35.json"}}], "type": "journal article", "published": "2020-01-27", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "60", "issue": "1", "pages": "322-331", "issn-l": "1549-9596"}, "abstract": "Biomolecular crowding affects the biophysical and biochemical behavior of macromolecules compared with the dilute environment in experiments on isolated proteins. Computational modeling and simulation are useful tools to study how crowding affects the structural dynamics and biological properties of macromolecules. With increases in computational power, modeling and simulation of large-scale all-atom explicit-solvent models of the prokaryote cytoplasm have now become possible. In this work, we built an atomistic model of the cytoplasm of Escherichia coli composed of 1.5 million atoms and submitted it to a total of 3 \u03bcs of molecular dynamics simulations. The model consisted of eight different proteins representing about 50% of the cytoplasmic proteins and one type of t-RNA molecule. Properties of biomolecules under crowding conditions were compared with those from simulations of the individual compounds under dilute conditions. The simulation model was found to be consistent with experimental data about the diffusion coefficient and stability of macromolecules under crowded conditions. In order to stimulate further work, we provide a Python script and a set of files to enable other researchers to build their own E. coli cytoplasm models to address questions related to crowding.", "doi": "10.1021/acs.jcim.9b00971", "pmid": "31816234", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-21T11:33:12.867Z", "modified": "2026-08-21T11:33:12.955Z"}, {"entity": "publication", "iuid": "5c2e0a3c71e64cc1b02eb426224748f8", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5c2e0a3c71e64cc1b02eb426224748f8.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5c2e0a3c71e64cc1b02eb426224748f8"}}, "title": "Sharing Data from Molecular Simulations.", "authors": [{"family": "Abraham", "given": "Mark", "initials": "M"}, {"family": "Apostolov", "given": "Rossen", "initials": "R"}, {"family": "Barnoud", "given": "Jonathan", "initials": "J"}, {"family": "Bauer", "given": "Paul", "initials": "P"}, {"family": "Blau", "given": "Christian", "initials": "C"}, {"family": "Bonvin", "given": "Alexandre M J J", "initials": "AMJJ", "orcid": "0000-0001-7369-1322", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4aeb5f598a22444dbd25594f236838cc.json"}}, {"family": "Chavent", "given": "Matthieu", "initials": "M", "orcid": "0000-0003-4524-4773", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4e5e4d37f7184fd8969c4af58a8565ef.json"}}, {"family": "Chodera", "given": "John", "initials": "J", "orcid": "0000-0003-0542-119X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb8cc357fe1d432aaf1cf675d7d4d966.json"}}, {"family": "\u010condi\u0107-Jurki\u0107", "given": "Karmen", "initials": "K"}, {"family": "Delemotte", "given": "Lucie", "initials": "L", "orcid": "0000-0002-0828-3899", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/87eaf619d7bd487ebdbe68c46b827e66.json"}}, {"family": "Grubm\u00fcller", "given": "Helmut", "initials": "H", "orcid": "0000-0002-3270-3144", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/077a1a9310cf48ddbef5adb5c6838dec.json"}}, {"family": "Howard", "given": "Rebecca J", "initials": "RJ"}, {"family": "Jordan", "given": "E Joseph", "initials": "EJ"}, {"family": "Lindahl", "given": "Erik", "initials": "E"}, {"family": "Ollila", "given": "O H Samuli", "initials": "OHS", "orcid": "0000-0002-8728-1006", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2a83b16eb9e54c599be006f8e7785bf7.json"}}, {"family": "Selent", "given": "Jana", "initials": "J", "orcid": "0000-0002-1844-4449", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/657ca056795a459b859dca9e77b2f222.json"}}, {"family": "Smith", "given": "Daniel G A", "initials": "DGA", "orcid": "0000-0001-8626-0900", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d4b76591095b47a9b36fff801ab55068.json"}}, {"family": "Stansfeld", "given": "Phillip J", "initials": "PJ", "orcid": "0000-0001-8800-7669", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5072c1d6c7524aa88fd5c4a27812358a.json"}}, {"family": "Tiemann", "given": "Johanna K S", "initials": "JKS"}, {"family": "Trellet", "given": "Mikael", "initials": "M"}, {"family": "Woods", "given": "Christopher", "initials": "C"}, {"family": "Zhmurov", "given": "Artem", "initials": "A", "orcid": "0000-0002-4414-8352", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1155b10296fb4792a6312aee53bc9829.json"}}], "type": "journal article", "published": "2019-10-28", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "issn-l": "1549-9596", "volume": "59", "issue": "10", "pages": "4093-4099"}, "abstract": "Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 ( https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/ ). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations.", "doi": "10.1021/acs.jcim.9b00665", "pmid": "31525920", "labels": {"Lucie Delemotte": null, "SciLifeLab Fellow": null}, "xrefs": [], "notes": [], "created": "2020-10-05T11:04:23.999Z", "modified": "2022-11-04T11:32:15.840Z"}, {"entity": "publication", "iuid": "b33466aa3061444b89ab8277479d638b", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b33466aa3061444b89ab8277479d638b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b33466aa3061444b89ab8277479d638b"}}, "title": "Force Field Benchmark of Amino Acids: I. Hydration and Diffusion in Different Water Models.", "authors": [{"family": "Zhang", "given": "Haiyang", "initials": "H"}, {"family": "Yin", "given": "Chunhua", "initials": "C"}, {"family": "Jiang", "given": "Yang", "initials": "Y"}, {"family": "van der Spoel", "given": "David", "initials": "D"}], "type": "journal article", "published": "2018-05-29", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "58", "issue": "5", "pages": "1037-1052", "issn-l": "1549-9596"}, "abstract": "Thermodynamic and kinetic properties are of critical importance for the applicability of computational models to biomolecules such as proteins. Here we present an extensive evaluation of the Amber ff99SB-ILDN force field for modeling of hydration and diffusion of amino acids with three-site (SPC, SPC/E, SPC/E", "doi": "10.1021/acs.jcim.8b00026", "pmid": "29648448", "labels": {"Affiliated researcher": null}, "xrefs": [], "notes": [], "created": "2018-12-05T12:39:13.775Z", "modified": "2018-12-05T12:39:13.793Z"}, {"entity": "publication", "iuid": "976b2c71aa6d4b6fbc0d14d4f4b85ac5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/976b2c71aa6d4b6fbc0d14d4f4b85ac5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/976b2c71aa6d4b6fbc0d14d4f4b85ac5"}}, "title": "Prediction of Ordered Water Molecules in Protein Binding Sites from Molecular Dynamics Simulations: The Impact of Ligand Binding on Hydration Networks.", "authors": [{"family": "Rudling", "given": "Axel", "initials": "A"}, {"family": "Orro", "given": "Adolfo", "initials": "A"}, {"family": "Carlsson", "given": "Jens", "initials": "J", "orcid": "0000-0003-4623-2977", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/792c1cd4aca248dc94b967115ffa82df.json"}}], "type": "journal article", "published": "2018-02-26", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "issn-l": "1549-9596", "volume": "58", "issue": "2", "pages": "350-361"}, "abstract": "Water plays a major role in ligand binding and is attracting increasing attention in structure-based drug design. Water molecules can make large contributions to binding affinity by bridging protein-ligand interactions or by being displaced upon complex formation, but these phenomena are challenging to model at the molecular level. Herein, networks of ordered water molecules in protein binding sites were analyzed by clustering of molecular dynamics (MD) simulation trajectories. Locations of ordered waters (hydration sites) were first identified from simulations of high resolution crystal structures of 13 protein-ligand complexes. The MD-derived hydration sites reproduced 73% of the binding site water molecules observed in the crystal structures. If the simulations were repeated without the cocrystallized ligands, a majority (58%) of the crystal waters in the binding sites were still predicted. In addition, comparison of the hydration sites obtained from simulations carried out in the absence of ligands to those identified for the complexes revealed that the networks of ordered water molecules were preserved to a large extent, suggesting that the locations of waters in a protein-ligand interface are mainly dictated by the protein. Analysis of >1000 crystal structures showed that hydration sites bridged protein-ligand interactions in complexes with different ligands, and those with high MD-derived occupancies were more likely to correspond to experimentally observed ordered water molecules. The results demonstrate that ordered water molecules relevant for modeling of protein-ligand complexes can be identified from MD simulations. Our findings could contribute to development of improved methods for structure-based virtual screening and lead optimization.", "doi": "10.1021/acs.jcim.7b00520", "pmid": "29308882", "labels": {"Affiliated researcher": null, "Jens Carlsson": null, "SciLifeLab Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC6716772"}], "notes": [], "created": "2018-12-03T14:45:20.276Z", "modified": "2022-11-04T11:32:17.936Z"}, {"entity": "publication", "iuid": "f32d2e20eba14d84a98d051d05ff3366", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f32d2e20eba14d84a98d051d05ff3366.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f32d2e20eba14d84a98d051d05ff3366"}}, "title": "Evaluation of Generalized Born Models for Large Scale Affinity Prediction of Cyclodextrin Host-Guest Complexes.", "authors": [{"family": "Zhang", "given": "Haiyang", "initials": "H"}, {"family": "Yin", "given": "Chunhua", "initials": "C"}, {"family": "Yan", "given": "Hai", "initials": "H"}, {"family": "van der Spoel", "given": "David", "initials": "D"}], "type": "journal article", "published": "2016-10-24", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "56", "issue": "10", "pages": "2080-2092", "issn-l": "1549-9596"}, "abstract": "Binding affinity prediction with implicit solvent models remains a challenge in virtual screening for drug discovery. In order to assess the predictive power of implicit solvent models in docking techniques with Amber scoring, three generalized Born models (GB", "doi": "10.1021/acs.jcim.6b00418", "pmid": "27626790", "labels": {"Affiliated researcher": null}, "xrefs": [], "notes": [], "created": "2018-12-05T11:44:49.405Z", "modified": "2018-12-05T11:44:49.425Z"}, {"entity": "publication", "iuid": "1c7680ee43ec494b9dcd408507b5aa92", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1c7680ee43ec494b9dcd408507b5aa92.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1c7680ee43ec494b9dcd408507b5aa92"}}, "title": "Exploration of Interfacial Hydration Networks of Target-Ligand Complexes.", "authors": [{"family": "Jeszen\u0151i", "given": "Norbert", "initials": "N"}, {"family": "B\u00e1lint", "given": "M\u00f3nika", "initials": "M"}, {"family": "Horv\u00e1th", "given": "Istv\u00e1n", "initials": "I"}, {"family": "van der Spoel", "given": "David", "initials": "D"}, {"family": "Het\u00e9nyi", "given": "Csaba", "initials": "C"}], "type": "journal article", "published": "2016-01-25", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "56", "issue": "1", "pages": "148-158", "issn-l": "1549-9596"}, "abstract": "Interfacial hydration strongly influences interactions between biomolecules. For example, drug-target complexes are often stabilized by hydration networks formed between hydrophilic residues and water molecules at the interface. Exhaustive exploration of hydration networks is challenging for experimental as well as theoretical methods due to high mobility of participating water molecules. In the present study, we introduced a tool for determination of the complete, void-free hydration structures of molecular interfaces. The tool was applied to 31 complexes including histone proteins, a HIV-1 protease, a G-protein-signaling modulator, and peptide ligands of various lengths. The complexes contained 344 experimentally determined water positions used for validation, and excellent agreement with these was obtained. High-level cooperation between interfacial water molecules was detected by a new approach based on the decomposition of hydration networks into static and dynamic network regions (subnets). Besides providing hydration structures at the atomic level, our results uncovered hitherto hidden networking fundaments of integrity and stability of complex biomolecular interfaces filling an important gap in the toolkit of drug design and structural biochemistry. The presence of continuous, static regions of the interfacial hydration network was found necessary also for stable complexes of histone proteins participating in chromatin assembly and epigenetic regulation. ", "doi": "10.1021/acs.jcim.5b00638", "pmid": "26704050", "labels": {"Affiliated researcher": null}, "xrefs": [], "notes": [], "created": "2018-12-05T11:47:30.446Z", "modified": "2018-12-05T11:47:30.465Z"}, {"entity": "publication", "iuid": "67d3cb3c7bf44cf7a70ae35f3a91c671", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/67d3cb3c7bf44cf7a70ae35f3a91c671.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/67d3cb3c7bf44cf7a70ae35f3a91c671"}}, "title": "Virtual Screening for Transition State Analogue Inhibitors of IRAP Based on Quantum Mechanically Derived Reaction Coordinates.", "authors": [{"family": "Svensson", "given": "Fredrik", "initials": "F"}, {"family": "Engen", "given": "Karin", "initials": "K"}, {"family": "Lundb\u00e4ck", "given": "Thomas", "initials": "T"}, {"family": "Larhed", "given": "Mats", "initials": "M"}, {"family": "Sk\u00f6ld", "given": "Christian", "initials": "C"}], "type": "journal article", "published": "2015-09-28", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "55", "issue": "9", "pages": "1984-1993", "issn-l": "1549-9596"}, "abstract": "Transition state and high energy intermediate mimetics have the potential to be very potent enzyme inhibitors. In this study, a model of peptide hydrolysis in the active site of insulin-regulated aminopeptidase (IRAP) was developed using density functional theory calculations and the cluster approach. The 3D structure models of the reaction coordinates were used for virtual screening to obtain new chemical starting points for IRAP inhibitors. This mechanism-based virtual screening process managed to identify several known peptidase inhibitors from a library of over 5 million compounds, and biological testing identified one compound not previously reported as an IRAP inhibitor. This novel methodology for virtual screening is a promising approach to identify new inhibitors mimicking key transition states or intermediates of an enzymatic reaction.", "doi": "10.1021/acs.jcim.5b00359", "pmid": "26252078", "labels": [], "xrefs": [], "notes": [], "created": "2018-12-05T12:19:04.140Z", "modified": "2026-08-21T11:33:10.830Z"}, {"entity": "publication", "iuid": "d9610bd320384d4395c79954dd026603", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d9610bd320384d4395c79954dd026603.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d9610bd320384d4395c79954dd026603"}}, "title": "Force Field Benchmark of Organic Liquids. 2. Gibbs Energy of Solvation.", "authors": [{"family": "Zhang", "given": "Jin", "initials": "J"}, {"family": "Tuguldur", "given": "Badamkhatan", "initials": "B"}, {"family": "van der Spoel", "given": "David", "initials": "D"}], "type": "journal article", "published": "2015-06-22", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "55", "issue": "6", "pages": "1192-1201", "issn-l": "1549-9596"}, "abstract": "Quantitative prediction of physical properties of liquids is a longstanding goal of molecular simulation. Here, we evaluate the predictive power of the Generalized Amber Force Field (Wang et al. J. Comput. Chem. 2004, 25, 1157-1174) for the Gibbs energy of solvation of organic molecules in organic solvents using the thermodynamics integration (TI) method. The results are compared to experimental data, to a model based on quantitative structure property relations (QSPR), and to the conductor-like screening models for realistic solvation (COSMO-RS) model. Although the TI calculations yield slightly better correlation to experimental results than the other models, in all fairness we should conclude that the difference between the models is minor since both QSPR and COSMO-RS yield a slightly lower RMSD from that of the experiment (<3.5 kJ/mol). By analyzing which molecules (either as solvents or solutes) are outliers in the TI calculations, we can pinpoint where additional parametrization efforts are needed. For the force field based TI calculations, deviations from the experiment occur in particular when compounds containing nitro or ester groups are solvated into other liquids, suggesting that the interaction between these groups and solvents may be too strong. In the COSMO-RS calculations, outliers mainly occur when compounds containing (in particular aromatic) rings are solvated despite using a ring correction term in the calculations.", "doi": "10.1021/acs.jcim.5b00106", "pmid": "26010106", "labels": [], "xrefs": [], "notes": [], "created": "2018-12-05T09:08:51.790Z", "modified": "2026-08-21T11:33:08.984Z"}, {"entity": "publication", "iuid": "592e605cfd8946119c1be44e3e4048fb", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/592e605cfd8946119c1be44e3e4048fb.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/592e605cfd8946119c1be44e3e4048fb"}}, "title": "Molecular docking screening using agonist-bound GPCR structures: probing the A2A adenosine receptor.", "authors": [{"family": "Rodr\u00edguez", "given": "David", "initials": "D"}, {"family": "Gao", "given": "Zhang-Guo", "initials": "ZG"}, {"family": "Moss", "given": "Steven M", "initials": "SM"}, {"family": "Jacobson", "given": "Kenneth A", "initials": "KA"}, {"family": "Carlsson", "given": "Jens", "initials": "J"}], "type": "journal article", "published": "2015-03-23", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "issn-l": "1549-9596", "volume": "55", "issue": "3", "pages": "550-563"}, "abstract": "Crystal structures of G protein-coupled receptors (GPCRs) have recently revealed the molecular basis of ligand binding and activation, which has provided exciting opportunities for structure-based drug design. The A2A adenosine receptor (A2AAR) is a promising therapeutic target for cardiovascular diseases, but progress in this area is limited by the lack of novel agonist scaffolds. We carried out docking screens of 6.7 million commercially available molecules against active-like conformations of the A2AAR to investigate whether these structures could guide the discovery of agonists. Nine out of the 20 predicted agonists were confirmed to be A2AAR ligands, but none of these activated the ARs. The difficulties in discovering AR agonists using structure-based methods originated from limited atomic-level understanding of the activation mechanism and a chemical bias toward antagonists in the screened library. In particular, the composition of the screened library was found to strongly reduce the likelihood of identifying AR agonists, which reflected the high ligand complexity required for receptor activation. Extension of this analysis to other pharmaceutically relevant GPCRs suggested that library screening may not be suitable for targets requiring a complex receptor-ligand interaction network. Our results provide specific directions for the future development of novel A2AAR agonists and general strategies for structure-based drug discovery.", "doi": "10.1021/ci500639g", "pmid": "25625646", "labels": [], "xrefs": [{"db": "mid", "key": "NIHMS699028"}, {"db": "pmc", "key": "PMC4474233"}], "notes": [], "created": "2018-12-03T14:36:44.636Z", "modified": "2026-08-21T11:37:33.150Z"}, {"entity": "publication", "iuid": "bbcb738c5868420d953f3eb82e1d1dd5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/bbcb738c5868420d953f3eb82e1d1dd5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/bbcb738c5868420d953f3eb82e1d1dd5"}}, "title": "Scaling predictive modeling in drug development with cloud computing.", "authors": [{"family": "Moghadam", "given": "Behrooz Torabi", "initials": "BT"}, {"family": "Alvarsson", "given": "Jonathan", "initials": "J"}, {"family": "Holm", "given": "Marcus", "initials": "M"}, {"family": "Eklund", "given": "Martin", "initials": "M"}, {"family": "Carlsson", "given": "Lars", "initials": "L"}, {"family": "Spjuth", "given": "Ola", "initials": "O"}], "type": "journal article", "published": "2015-01-26", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "55", "issue": "1", "pages": "19-25", "issn-l": "1549-9596"}, "abstract": "Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.", "doi": "10.1021/ci500580y", "pmid": "25493610", "labels": [], "xrefs": [], "notes": [], "created": "2018-12-05T11:52:34.627Z", "modified": "2026-08-21T11:37:31.125Z"}, {"entity": "publication", "iuid": "37b7725a14e940db8d46fda784c221c4", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/37b7725a14e940db8d46fda784c221c4.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/37b7725a14e940db8d46fda784c221c4"}}, "title": "Benchmarking study of parameter variation when using signature fingerprints together with support vector machines.", "authors": [{"family": "Alvarsson", "given": "Jonathan", "initials": "J"}, {"family": "Eklund", "given": "Martin", "initials": "M"}, {"family": "Andersson", "given": "Claes", "initials": "C"}, {"family": "Carlsson", "given": "Lars", "initials": "L"}, {"family": "Spjuth", "given": "Ola", "initials": "O"}, {"family": "Wikberg", "given": "Jarl E S", "initials": "JE"}], "type": "journal article", "published": "2014-11-24", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "54", "issue": "11", "pages": "3211-3217", "issn-l": "1549-9596"}, "abstract": "QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, \u03b3, and signature height. C is a penalty parameter that limits overfitting, \u03b3 controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and \u03b3 in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.", "doi": "10.1021/ci500344v", "pmid": "25318024", "labels": [], "xrefs": [], "notes": [], "created": "2018-12-05T10:06:55.145Z", "modified": "2026-08-21T11:37:27.300Z"}, {"entity": "publication", "iuid": "34f2f00b21224f1dacf5b7175916ad71", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/34f2f00b21224f1dacf5b7175916ad71.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/34f2f00b21224f1dacf5b7175916ad71"}}, "title": "Ligand-based target prediction with signature fingerprints.", "authors": [{"family": "Alvarsson", "given": "Jonathan", "initials": "J"}, {"family": "Eklund", "given": "Martin", "initials": "M"}, {"family": "Engkvist", "given": "Ola", "initials": "O"}, {"family": "Spjuth", "given": "Ola", "initials": "O"}, {"family": "Carlsson", "given": "Lars", "initials": "L"}, {"family": "Wikberg", "given": "Jarl E S", "initials": "JE"}, {"family": "Noeske", "given": "Tobias", "initials": "T"}], "type": "journal article", "published": "2014-10-27", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "54", "issue": "10", "pages": "2647-2653", "issn-l": "1549-9596"}, "abstract": "When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.", "doi": "10.1021/ci500361u", "pmid": "25230336", "labels": [], "xrefs": [], "notes": [], "created": "2018-12-05T09:38:36.515Z", "modified": "2026-08-21T11:37:29.075Z"}, {"entity": "publication", "iuid": "0f03d21c0da141c4b8f83c97d7a5f7bc", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/0f03d21c0da141c4b8f83c97d7a5f7bc.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/0f03d21c0da141c4b8f83c97d7a5f7bc"}}, "title": "Strategies for improved modeling of GPCR-drug complexes: blind predictions of serotonin receptors bound to ergotamine.", "authors": [{"family": "Rodr\u00edguez", "given": "David", "initials": "D"}, {"family": "Ranganathan", "given": "Anirudh", "initials": "A"}, {"family": "Carlsson", "given": "Jens", "initials": "J"}], "type": "journal article", "published": "2014-07-28", "journal": {"volume": "54", "issn": "1549-960X", "issue": "7", "pages": "2004-2021", "title": "J Chem Inf Model", "issn-l": "1549-9596"}, "abstract": "The recent increase in the number of atomic-resolution structures of G protein-coupled receptors (GPCRs) has contributed to a deeper understanding of ligand binding to several important drug targets. However, reliable modeling of GPCR-ligand complexes for the vast majority of receptors with unknown structure remains to be one of the most challenging goals for computer-aided drug design. The GPCR Dock 2013 assessment, in which researchers were challenged to predict the crystallographic structures of serotonin 5-HT(1B) and 5-HT(2B) receptors bound to ergotamine, provided an excellent opportunity to benchmark the current state of this field. Our contributions to GPCR Dock 2013 accurately predicted the binding mode of ergotamine with RMSDs below 1.8 \u00c5 for both receptors, which included the best submissions for the 5-HT(1B) complex. Our models also had the most accurate description of the binding sites and receptor-ligand contacts. These results were obtained using a ligand-guided homology modeling approach, which combines extensive molecular docking screening with incorporation of information from multiple crystal structures and experimentally derived restraints. In this work, we retrospectively analyzed thousands of structures that were generated during the assessment to evaluate our modeling strategies. Major contributors to accuracy were found to be improved modeling of extracellular loop two in combination with the use of molecular docking to optimize the binding site for ligand recognition. Our results suggest that modeling of GPCR-drug complexes has reached a level of accuracy at which structure-based drug design could be applied to a large number of pharmaceutically relevant targets.", "doi": "10.1021/ci5002235", "pmid": "25030302", "labels": [], "xrefs": [], "notes": [], "created": "2018-12-03T14:34:47.824Z", "modified": "2026-08-21T11:37:25.501Z"}, {"entity": "publication", "iuid": "47e06f5e7e80415389445c51bd9a96a0", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/47e06f5e7e80415389445c51bd9a96a0.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/47e06f5e7e80415389445c51bd9a96a0"}}, "title": "Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A(2A) adenosine receptor.", "authors": [{"family": "Chen", "given": "Dan", "initials": "D"}, {"family": "Ranganathan", "given": "Anirudh", "initials": "A"}, {"family": "IJzerman", "given": "Adriaan P", "initials": "AP"}, {"family": "Siegal", "given": "Gregg", "initials": "G"}, {"family": "Carlsson", "given": "Jens", "initials": "J"}], "type": "journal article", "published": "2013-10-28", "journal": {"volume": "53", "issn": "1549-960X", "issue": "10", "pages": "2701-2714", "title": "J Chem Inf Model", "issn-l": "1549-9596"}, "abstract": "Fragment-based lead discovery (FBLD) is becoming an increasingly important method in drug development. We have explored the potential to complement NMR-based biophysical screening of chemical libraries with molecular docking in FBLD against the A(2A) adenosine receptor (A(2A)AR), a drug target for inflammation and Parkinson's disease. Prior to an NMR-based screen of a fragment library against the A(2A)AR, molecular docking against a crystal structure was used to rank the same set of molecules by their predicted affinities. Molecular docking was able to predict four out of the five orthosteric ligands discovered by NMR among the top 5% of the ranked library, suggesting that structure-based methods could be used to prioritize among primary hits from biophysical screens. In addition, three fragments that were top-ranked by molecular docking, but had not been picked up by the NMR-based method, were demonstrated to be A(2A)AR ligands. While biophysical approaches for fragment screening are typically limited to a few thousand compounds, the docking screen was extended to include 328,000 commercially available fragments. Twenty-two top-ranked compounds were tested in radioligand binding assays, and 14 of these were A(2A)AR ligands with K(i) values ranging from 2 to 240 \u03bcM. Optimization of fragments was guided by molecular dynamics simulations and free energy calculations. The results illuminate strengths and weaknesses of molecular docking and demonstrate that this method can serve as a valuable complementary tool to biophysical screening in FBLD.", "doi": "10.1021/ci4003156", "pmid": "23971943", "labels": [], "xrefs": [], "notes": [], "created": "2018-12-03T14:32:44.068Z", "modified": "2026-08-21T11:37:23.311Z"}], "created": "2018-12-05T09:08:51.805Z", "modified": "2020-11-27T13:12:55.500Z"}