Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.

ABSTRACT: Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property plays a critical role. Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out. In particular, regression and classification models for three different issues are inferred using both methods under different experimental scenarios: two drug-like properties, such as blood-brain-barrier and human intestinal absorption, and enantiomeric excess, as a measurement of purity used for

SUBMITTER: Ponzoni I 

PROVIDER: S-EPMC5445096 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature