EMPIRICAL COMPARISON AND ANALYSIS OF MACHINE LEARNING-BASED APPROACHES FOR DRUGGABLE PROTEIN IDENTIFICATION
Issued Date
2023-01-02
Resource Type
eISSN
16112156
Scopus ID
2-s2.0-85170373824
Journal Title
EXCLI Journal
Volume
22
Start Page
915
End Page
927
Rights Holder(s)
SCOPUS
Bibliographic Citation
EXCLI Journal Vol.22 (2023) , 915-927
Suggested Citation
Shoombuatong W., Schaduangrat N., Nikom J. EMPIRICAL COMPARISON AND ANALYSIS OF MACHINE LEARNING-BASED APPROACHES FOR DRUGGABLE PROTEIN IDENTIFICATION. EXCLI Journal Vol.22 (2023) , 915-927. 927. doi:10.17179/excli2023-6410 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/90052
Title
EMPIRICAL COMPARISON AND ANALYSIS OF MACHINE LEARNING-BASED APPROACHES FOR DRUGGABLE PROTEIN IDENTIFICATION
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Efficiently and precisely identifying drug targets is crucial for developing and discovering potential medications. While conventional experimental approaches can accurately pinpoint these targets, they suffer from time constraints and are not easily adaptable to high-throughput processes. On the other hand, computational approaches, particularly those utilizing machine learning (ML), offer an efficient means to accelerate the prediction of drugga-ble proteins based solely on their primary sequences. Recently, several state-of-the-art computational methods have been developed for predicting and analyzing druggable proteins. These computational methods showed high diversity in terms of benchmark datasets, feature extraction schemes, ML algorithms, evaluation strategies and webserver/software usability. Thus, our objective is to reexamine these computational approaches and conduct a comprehensive assessment of their strengths and weaknesses across multiple aspects. In this study, we deliver the first comprehensive survey regarding the state-of-the-art computational approaches for in silico prediction of drug-gable proteins. First, we provided information regarding the existing benchmark datasets and the types of ML methods employed. Second, we investigated the effectiveness of these computational methods in druggable protein identification for each benchmark dataset. Third, we summarized the important features used in this field and the existing webserver/software. Finally, we addressed the present constraints of the existing methods and offer valu-able guidance to the scientific community in designing and developing novel prediction models. We anticipate that this comprehensive review will provide crucial information for the development of more accurate and efficient druggable protein predictors.