Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

By A. Rifaioglu, Heval Atas, Maria Jesus Martin, Rengül Çetin-Atalay, Volkan Atalay, Tunca Dogan. Briefings Bioinform., 2018

450 citations recorded by the source corpus.

Abstract The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as ‘virtual screening’ (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4d157bec76125b1121a999ab2b79730540ab7aad

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Citations in the corpus

Machine Learning Methods in Drug Discovery (2020, background): “Supervised and unsupervised learning can be combined as semi-supervised and reinforcement learning, where both functions can be utilized for various data sets [28].”
Machine learning applications in drug development (2019, background): “…discovery: for example, drug candidate identiﬁcation via molecule docking, in order to predict and preselect interesting drug-target interactions for further research [43]; and protein engineering, that is, de novo molecular design of proteins with speciﬁc expected binding or motif functions [44].”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, background): “More details about applying deep learning in drug discovery can be found in [126].”