Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework
Issued Date
2022-09-16
Resource Type
eISSN
25890042
Scopus ID
2-s2.0-85136472726
Journal Title
iScience
Volume
25
Issue
9
Rights Holder(s)
SCOPUS
Bibliographic Citation
iScience Vol.25 No.9 (2022)
Suggested Citation
Charoenkwan P., Schaduangrat N., Lio’ P., Moni M.A., Shoombuatong W., Manavalan B. Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. iScience Vol.25 No.9 (2022). doi:10.1016/j.isci.2022.104883 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/86471
Title
Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework
Other Contributor(s)
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 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.