{"entity": "journal", "iuid": "e7e85692ee8443cc8acb8547290b6b86", "timestamp": "2026-08-15T12:59:21.517Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/J%20Cheminform.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/J%20Cheminform"}}, "title": "J Cheminform", "issn": "1758-2946", "issn-l": "1758-2946", "publications_count": 5, "publications": [{"entity": "publication", "iuid": "1291a8b321634882844a3e6520ab3515", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1291a8b321634882844a3e6520ab3515.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1291a8b321634882844a3e6520ab3515"}}, "title": "Using informative features in machine learning based method for COVID-19 drug repurposing.", "authors": [{"family": "Aghdam", "given": "Rosa", "initials": "R", "orcid": "0000-0001-9045-9592", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0179bdcc8b8e4bd68549403ed5b66b79.json"}}, {"family": "Habibi", "given": "Mahnaz", "initials": "M", "orcid": "0000-0002-8969-2706", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f04af4e059814b78bfb107c3a5782f70.json"}}, {"family": "Taheri", "given": "Golnaz", "initials": "G", "orcid": "0000-0002-2741-0355", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/014c217121d346b2b371bbc1c2fede57.json"}}], "type": "journal article", "published": "2021-09-20", "journal": {"title": "J Cheminform", "issn": "1758-2946", "issn-l": "1758-2946", "volume": "13", "issue": "1", "pages": "70"}, "abstract": "Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.", "doi": "10.1186/s13321-021-00553-9", "pmid": "34544500", "labels": {"Golnaz Taheri": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC8451172"}, {"db": "pii", "key": "10.1186/s13321-021-00553-9"}], "notes": [], "created": "2025-03-21T09:08:40.624Z", "modified": "2025-03-21T10:36:00.374Z"}, {"entity": "publication", "iuid": "6d55cf0e74e2450a8e20a25488188449", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/6d55cf0e74e2450a8e20a25488188449.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/6d55cf0e74e2450a8e20a25488188449"}}, "title": "Systematic exploration of multiple drug binding sites.", "authors": [{"family": "B\u00e1lint", "given": "M\u00f3nika", "initials": "M"}, {"family": "Jeszen\u0151i", "given": "Norbert", "initials": "N"}, {"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": "2017-12-28", "journal": {"title": "J Cheminform", "issn": "1758-2946", "volume": "9", "issue": "1", "pages": "65", "issn-l": "1758-2946"}, "abstract": "Targets with multiple (prerequisite or allosteric) binding sites have an increasing importance in drug design. Experimental determination of atomic resolution structures of ligands weakly bound to multiple binding sites is often challenging. Blind docking has been widely used for fast mapping of the entire target surface for multiple binding sites. Reliability of blind docking is limited by approximations of hydration models, simplified handling of molecular flexibility, and imperfect search algorithms.\n\nTo overcome such limitations, the present study introduces Wrap 'n' Shake (WnS), an atomic resolution method that systematically \"wraps\" the entire target into a monolayer of ligand molecules. Functional binding sites are extracted by a rapid molecular dynamics shaker. WnS is tested on biologically important systems such as mitogen-activated protein, tyrosine-protein kinases, key players of cellular signaling, and farnesyl pyrophosphate synthase, a target of antitumor agents.", "doi": "10.1186/s13321-017-0255-6", "pmid": "29282592", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "10.1186/s13321-017-0255-6"}, {"db": "pmc", "key": "PMC5745209"}], "notes": [], "created": "2018-12-05T12:51:07.212Z", "modified": "2018-12-05T12:51:07.230Z"}, {"entity": "publication", "iuid": "3c1521b31043417f97a985f3db9f54f0", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/3c1521b31043417f97a985f3db9f54f0.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/3c1521b31043417f97a985f3db9f54f0"}}, "title": "Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.", "authors": [{"family": "Lampa", "given": "Samuel", "initials": "S"}, {"family": "Alvarsson", "given": "Jonathan", "initials": "J"}, {"family": "Spjuth", "given": "Ola", "initials": "O"}], "type": "journal article", "published": "2016-11-24", "journal": {"title": "J Cheminform", "issn": "1758-2946", "volume": "8", "issue": null, "pages": "67", "issn-l": "1758-2946"}, "abstract": "Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.", "doi": "10.1186/s13321-016-0179-6", "pmid": "27942268", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "179"}, {"db": "pmc", "key": "PMC5123367"}], "notes": [], "created": "2018-12-05T12:32:51.136Z", "modified": "2018-12-05T12:32:51.155Z"}, {"entity": "publication", "iuid": "9ebf765a514f4602a8acfd0954f37fe3", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9ebf765a514f4602a8acfd0954f37fe3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9ebf765a514f4602a8acfd0954f37fe3"}}, "title": "XMetDB: an open access database for xenobiotic metabolism.", "authors": [{"family": "Spjuth", "given": "Ola", "initials": "O"}, {"family": "Rydberg", "given": "Patrik", "initials": "P"}, {"family": "Willighagen", "given": "Egon L", "initials": "EL"}, {"family": "Evelo", "given": "Chris T", "initials": "CT"}, {"family": "Jeliazkova", "given": "Nina", "initials": "N"}], "type": "journal article", "published": "2016-09-15", "journal": {"title": "J Cheminform", "issn": "1758-2946", "volume": "8", "issue": null, "pages": "47", "issn-l": "1758-2946"}, "abstract": "Xenobiotic metabolism is an active research topic but the limited amount of openly available high-quality biotransformation data constrains predictive modeling. Current database often default to commonly available information: which enzyme metabolizes a compound, but neither experimental conditions nor the atoms that undergo metabolization are captured. We present XMetDB, an open access database for drugs and other xenobiotics and their respective metabolites. The database contains chemical structures of xenobiotic biotransformations with substrate atoms annotated as reaction centra, the resulting product formed, and the catalyzing enzyme, type of experiment, and literature references. Associated with the database is a web interface for the submission and retrieval of experimental metabolite data for drugs and other xenobiotics in various formats, and a web API for programmatic access is also available. The database is open for data deposition, and a curation scheme is in place for quality control. An extensive guide on how to enter experimental data into is available from the XMetDB wiki. XMetDB formalizes how biotransformation data should be reported, and the openly available systematically labeled data is a big step forward towards better models for predictive metabolism. ", "doi": "10.1186/s13321-016-0161-3", "pmid": "27651835", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "161"}, {"db": "pmc", "key": "PMC5025591"}], "notes": [], "created": "2018-12-05T12:16:02.700Z", "modified": "2018-12-05T12:16:02.719Z"}, {"entity": "publication", "iuid": "aacbcc9524394fd5a93322c848f9c7c1", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/aacbcc9524394fd5a93322c848f9c7c1.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/aacbcc9524394fd5a93322c848f9c7c1"}}, "title": "Large-scale ligand-based predictive modelling using support vector machines.", "authors": [{"family": "Alvarsson", "given": "Jonathan", "initials": "J"}, {"family": "Lampa", "given": "Samuel", "initials": "S"}, {"family": "Schaal", "given": "Wesley", "initials": "W"}, {"family": "Andersson", "given": "Claes", "initials": "C"}, {"family": "Wikberg", "given": "Jarl E S", "initials": "JE"}, {"family": "Spjuth", "given": "Ola", "initials": "O"}], "type": "journal article", "published": "2016-08-10", "journal": {"title": "J Cheminform", "issn": "1758-2946", "volume": "8", "issue": null, "pages": "39", "issn-l": "1758-2946"}, "abstract": "The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2\u00a0million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse. ", "doi": "10.1186/s13321-016-0151-5", "pmid": "27516811", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "151"}, {"db": "pmc", "key": "PMC4980776"}], "notes": [], "created": "2018-12-05T11:18:41.751Z", "modified": "2018-12-05T11:18:41.784Z"}], "created": "2018-12-05T11:18:41.765Z", "modified": "2020-11-27T13:12:56.992Z"}