Systematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysis.

ABSTRACT: Support vector machines are a popular machine learning method for many classification tasks in biology and chemistry. In addition, the support vector regression (SVR) variant is widely used for numerical property predictions. In chemoinformatics and pharmaceutical research, SVR has become the probably most popular approach for modeling of non-linear structure-activity relationships (SARs) and predicting compound potency values. Herein, we have systematically generated and analyzed SVR prediction models for a variety of compound data sets with different SAR characteristics. Although these SVR models were accurate on the basis of global prediction statistics and not prone to overfitting, they were found to consistently mispredict highly potent compounds. Hence, in regions of local SAR discon

SUBMITTER: Balfer J 

PROVIDER: S-EPMC4350943 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature