Accurate prediction of the drug-target affinity (DTA) in silico is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment. The most promising of them are based on deep learning techniques and graph neural networks to encode molecular structures. The recent breakthrough in protein structure prediction made by AlphaFold made an unprecedented amount of proteins without experimentally defined structures accessible for computational DTA prediction. In this work, we propose a new deep learning DTA model 3DProtDTA, which utilises AlphaFold structure predictions in conjunction with the graph representation of proteins. The model is superior to its rivals on common benchmarking datasets and has potential for further improvement.

Department of Biophysics and Medical Informatics Educational and Scientific Centre Institute of Biology and Medicine Taras Shevchenko National University of Kyiv 64 Volodymyrska Str 01601 Kyiv Ukraine

Department of Physics of Biological Systems Institute of Physics of The National Academy of Sciences of Ukraine Nauky Ave 46 03038 Kyiv Ukraine

Institute of Organic Chemistry and Biochemistry Czech Academy of Sciences CZ 166 10 Prague 6 Czech Republic

Receptor AI Inc 20 22 Wenlock Road London N1 7GU UK

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