A confidence predictor for logD using conformal regression and a support-vector machine

Abstract

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 $$\hbox {Q}^{2}=0.973$$ Q2=0.973 and with the best performing nonconformity measure having median prediction interval of $$\pm ~0.39$$ ±0.39 log units at 80% confidence and $$\pm ~0.60$$ ±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.

Keywords

• Conformal prediction
• Machine learning
• QSAR
• Support-vector machine
• LogD
• RDF