Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server.

ABSTRACT: BACKGROUND:The identification of target molecules is important for understanding the mechanism of "target deconvolution" in phenotypic screening and "polypharmacology" of drugs. Because conventional methods of identifying targets require time and cost, in-silico target identification has been considered an alternative solution. One of the well-known in-silico methods of identifying targets involves structure activity relationships (SARs). SARs have advantages such as low computational cost and high feasibility; however, the data dependency in the SAR approach causes imbalance of active data and ambiguity of inactive data throughout targets. RESULTS:We developed a ligand-based virtual screening model comprising 1121 target SAR models built using a random forest algorithm. The performance of

SUBMITTER: Lee K 

PROVIDER: S-EPMC5751401 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature