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Lapins M, Arvidsson S, Lampa S, et al. A confidence predictor for logD using conformal regression and a support-vector machine. J Cheminform. 2018;10(1):17doi: 10.1186/s13321-018-0271-1.
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), 17. https://doi.org/10.1186/s13321-018-0271-1
Lapins, Maris, et al. "A confidence predictor for logD using conformal regression and a support-vector machine." Journal of cheminformatics vol. 10,1 (2018): 17. doi: https://doi.org/10.1186/s13321-018-0271-1
Lapins M, Arvidsson S, Lampa S, Berg A, Schaal W, Alvarsson J, Spjuth O. A confidence predictor for logD using conformal regression and a support-vector machine. J Cheminform. 2018 Apr 03;10(1):17. doi: 10.1186/s13321-018-0271-1. PMID: 29616425; PMCID: PMC5882484.
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J Cheminform. 2018 Apr 03;10(1):17. doi: 10.1186/s13321-018-0271-1.

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

Journal of cheminformatics

Maris Lapins, Staffan Arvidsson, Samuel Lampa, Arvid Berg, Wesley Schaal, Jonathan Alvarsson, Ola Spjuth

Affiliations

  1. Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden.
  2. Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden. [email protected].

PMID: 29616425 PMCID: PMC5882484 DOI: 10.1186/s13321-018-0271-1

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 [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] 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; LogD; Machine learning; QSAR; RDF; Support-vector machine

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