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• W2798936682 abstract "Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water–octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of $$hbox {Q}^{2}=0.973$$ and with the best performing nonconformity measure having median prediction interval of $$pm ~0.39$$ log units at 80% confidence and $$pm ~0.60$$ log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service." @default.
• W2798936682 created "2018-05-07" @default.
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• W2798936682 date "2018-04-03" @default.
• W2798936682 modified "2023-10-03" @default.
• W2798936682 title "A confidence predictor for logD using conformal regression and a support-vector machine" @default.
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• W2798936682 doi "https://doi.org/10.1186/s13321-018-0271-1" @default.
• W2798936682 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5882484" @default.
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