Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
By Maryam Bagherian, Elyas Sabeti, Kai Wang, Maureen A. Sartor, Zaneta Nikolovska-Coleska, Kayvan Najarian. Briefings Bioinform., 2020
366 citations recorded by the source corpus.
Abstract The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology
View Paper: https://www.semanticscholar.org/paper/9cfd7941caf2ac4259fa08bccb80989a5eaa4319
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Citations in the corpus
- Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022, neutral): “Therefore, in silico approaches have gained increasing attention and made much progress in the last few years 5,6 .”
- Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design (2023, background): “By training on data that include information about genetic, proteomic, or transcriptomic features and their relationship to drug response or disease progression, the model can learn patterns and identify potential targets for further investigation [40].”