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 Q 2= 0.973 and with the best performing nonconformity measure having median prediction interval of ±0.39 log units at 80% confidence and ±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.[Figure not available: see fulltext.].
CITATION STYLE
Lapins, M., Arvidsson, S., Lampa, S., Berg, A., Schaal, W., Alvarsson, J., & Spjuth, O. (2018). A confidence predictor for logD using conformal regression and a support-vector machine. Journal of Cheminformatics, 10(1). https://doi.org/10.1186/s13321-018-0271-1
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