ChEMBL Group

Hinxton, United Kingdom

ChEMBL Group

Hinxton, United Kingdom
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van Westen G.J.P.,Leiden Amsterdam Center for Drug Research | van Westen G.J.P.,ChEMBL Group | Hendriks A.,Leiden Amsterdam Center for Drug Research | Wegner J.K.,Tibotec BVBA | And 4 more authors.
PLoS Computational Biology | Year: 2013

Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions. © 2013 van Westen et al.


Kruger F.A.,ChEMBL Group | Gaulton A.,ChEMBL Group | Nowotka M.,ChEMBL Group | Overington J.P.,ChEMBL Group
Bioinformatics | Year: 2015

PPDMs is a resource that maps small molecule bioactivities to protein domains from the Pfam-A collection of protein families. Small molecule bioactivities mapped to protein domains add important precision to approaches that use protein sequence searches alignments to assist applications in computational drug discovery and systems and chemical biology. We have previously proposed a mapping heuristic for a subset of bioactivities stored in ChEMBL with the Pfam-A domain most likely to mediate small molecule binding. We have since refined this mapping using a manual procedure. Here, we present a resource that provides up-to-date mappings and the possibility to review assigned mappings as well as to participate in their assignment and curation. We also describe how mappings provided through the PPDMs resource are made accessible through the main schema of the ChEMBL database. © The Author 2014. Published by Oxford University Press. All rights reserved.


PubMed | ChEMBL group
Type: Journal Article | Journal: Bioinformatics (Oxford, England) | Year: 2015

PPDMs is a resource that maps small molecule bioactivities to protein domains from the Pfam-A collection of protein families. Small molecule bioactivities mapped to protein domains add important precision to approaches that use protein sequence searches alignments to assist applications in computational drug discovery and systems and chemical biology. We have previously proposed a mapping heuristic for a subset of bioactivities stored in ChEMBL with the Pfam-A domain most likely to mediate small molecule binding. We have since refined this mapping using a manual procedure. Here, we present a resource that provides up-to-date mappings and the possibility to review assigned mappings as well as to participate in their assignment and curation. We also describe how mappings provided through the PPDMs resource are made accessible through the main schema of the ChEMBL database.The PPDMs resource and curation interface is available at https://www.ebi.ac.uk/chembl/research/ppdms/pfam_maps. The source-code for PPDMs is available under the Apache license at https://github.com/chembl/pfam_maps. Source code is available at https://github.com/chembl/pfam_map_loader to demonstrate the integration process with the main schema of ChEMBL.

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