Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities.
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Computational Bioscience Research Center (CBRC)
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/660575
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AbstractThe drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.
CitationThafar, M., Raies, A. B., Albaradei, S., Essack, M., & Bajic, V. B. (2019). Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities. Frontiers in Chemistry, 7. doi:10.3389/fchem.2019.00782
SponsorsThe research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).
PublisherFrontiers Media SA
JournalFrontiers in chemistry
Except where otherwise noted, this item's license is described as Archived with thanks to Frontiers in chemistry