Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework.

ABSTRACT: Discovery of potential drugs requires rapid and precise identification of drug targets. Although traditional experimental methodologies can accurately identify drug targets, they are time-consuming and inappropriate for high-throughput screening. Computational approaches based on machine learning (ML) algorithms can expedite the prediction of druggable proteins; however, the performance of the existing computational methods remains unsatisfactory. This study proposes a computational tool, SPIDER, to enhance the accurate prediction of druggable proteins. SPIDER employs various feature descriptors pertaining to several aspects, including physicochemical properties, compositional information, and composition-transition-distribution information, coupled with well-known ML algorithms to facilit

SUBMITTER: Charoenkwan P 

PROVIDER: S-EPMC9421381 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature