Journal
ISCIENCE
Volume 25, Issue 9, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.isci.2022.104883
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Funding
- College of Arts, Media and Technology, Chiang Mai University
- Chiang Mai University
- Mahidol University
- National Research Foundation of Korea (NRF) - Korean government (MSIT) [2021R1A2C1014338]
- SungKyunKwan University
- BK21 FOUR - Ministry of Education (MOE)
- NRF
- National Research Foundation of Korea [2021R1A2C1014338] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This study introduces a computational tool, SPIDER, for accurate prediction of druggable proteins. By employing various feature descriptors and machine learning algorithms, SPIDER enables more precise and robust prediction compared to baseline models and existing methods in an independent test dataset.
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.
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