Evaluating parameters for ligand-based modeling with random forest on sparse data sets.

ABSTRACT: Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study we analyzed four data sets and studied the efficiency of machine learning methods on sparse data structures, utilizing Morgan fingerprints of different radii and hash sizes, and compared with molecular signatures descriptor of different height. We specifically evaluated the effect these parameters had on modeling time, predictive performance, and memory requirements using two implementations of random forest; Scikit-learn as well as FEST. We also compared with a support vector machine implementa

SUBMITTER: Kensert A 

PROVIDER: S-EPMC6755600 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature