Modelling compound cytotoxicity using conformal prediction and PubChem HTS data.

ABSTRACT: The assessment of compound cytotoxicity is an important part of the drug discovery process. Accurate predictions of cytotoxicity have the potential to expedite decision making and save considerable time and effort. In this work we apply class conditional conformal prediction to model the cytotoxicity of compounds based on 16 high throughput cytotoxicity assays from PubChem. The data span 16 cell lines and comprise more than 440 000 unique compounds. The data sets are heavily imbalanced with only 0.8% of the tested compounds being cytotoxic. We trained one classification model for each cell line and validated the performance with respect to validity and accuracy. The generated models deliver high quality predictions for both toxic and non-toxic compounds despite the imbalance between the tw

SUBMITTER: Svensson F 

PROVIDER: S-EPMC6061930 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature