摘要：SummaryDiscovery 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 facilitate the construction of the final meta-predictor. The experimental results showed that SPIDER enabled more precise and robust prediction of druggable proteins than the baseline models and current existing methods in terms of the independent test dataset. An online web server was established and made freely available online.Graphical abstractDisplay OmittedHighlights•Computational models can expedite the identification of potential druggable proteins•SPIDER represents the first stacked model proposed for druggable protein prediction•SPIDER enables more precise prediction of druggable proteins than existing methods•The SPIDER web server is available athttp://pmlabstack.pythonanywhere.com/SPIDER.Artificial intelligence; Artificial intelligence applications; Computational chemistry; Drugs
