Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design

Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins. Because of continuing advances in structural biology, putative binding sites for small organic mole...

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Published in:	Frontiers in pharmacology Vol. 13; p. 837715
Main Authors:	Shi, Wentao, Singha, Manali, Srivastava, Gopal, Pu, Limeng, Ramanujam, J., Brylinski, Michal
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 11.03.2022
Subjects:	deep learning drug discovery and development graph neural network in silico drug design ligand binding sites Pharmacology recurrent neural network deep learning recurrent neural network drug discovery and development generative model graph neural network in silico drug design machine learning ligand binding sites
ISSN:	1663-9812, 1663-9812
Online Access:	Get full text
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Summary:	Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins. Because of continuing advances in structural biology, putative binding sites for small organic molecules are being discovered in numerous proteins linked to various diseases. These valuable data offer new opportunities to build efficient computational models predicting binding molecules for target sites through the application of data mining and machine learning. In particular, deep neural networks are powerful techniques capable of learning from complex data in order to make informed drug binding predictions. In this communication, we describe Pocket2Drug, a deep graph neural network model to predict binding molecules for a given a ligand binding site. This approach first learns the conditional probability distribution of small molecules from a large dataset of pocket structures with supervised training, followed by the sampling of drug candidates from the trained model. Comprehensive benchmarking simulations show that using Pocket2Drug significantly improves the chances of finding molecules binding to target pockets compared to traditional drug selection procedures. Specifically, known binders are generated for as many as 80.5% of targets present in the testing set consisting of dissimilar data from that used to train the deep graph neural network model. Overall, Pocket2Drug is a promising computational approach to inform the discovery of novel biopharmaceuticals.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Adriano D. Andricopulo, University of Sao Paulo, Brazil Reviewed by: Jahan B. Ghasemi, University of Tehran, Iran Guang Hu, Soochow University, China This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology
ISSN:	1663-9812 1663-9812
DOI:	10.3389/fphar.2022.837715