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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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Autores principales: Shi, Wentao, Singha, Manali, Srivastava, Gopal, Pu, Limeng, Ramanujam, J., Brylinski, Michal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962739/
https://www.ncbi.nlm.nih.gov/pubmed/35359869
http://dx.doi.org/10.3389/fphar.2022.837715
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author Shi, Wentao
Singha, Manali
Srivastava, Gopal
Pu, Limeng
Ramanujam, J.
Brylinski, Michal
author_facet Shi, Wentao
Singha, Manali
Srivastava, Gopal
Pu, Limeng
Ramanujam, J.
Brylinski, Michal
author_sort Shi, Wentao
collection PubMed
description 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.
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spelling pubmed-89627392022-03-30 Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design Shi, Wentao Singha, Manali Srivastava, Gopal Pu, Limeng Ramanujam, J. Brylinski, Michal Front Pharmacol Pharmacology 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. Frontiers Media S.A. 2022-03-11 /pmc/articles/PMC8962739/ /pubmed/35359869 http://dx.doi.org/10.3389/fphar.2022.837715 Text en Copyright © 2022 Shi, Singha, Srivastava, Pu, Ramanujam and Brylinski. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Shi, Wentao
Singha, Manali
Srivastava, Gopal
Pu, Limeng
Ramanujam, J.
Brylinski, Michal
Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design
title Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design
title_full Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design
title_fullStr Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design
title_full_unstemmed Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design
title_short Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design
title_sort pocket2drug: an encoder-decoder deep neural network for the target-based drug design
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962739/
https://www.ncbi.nlm.nih.gov/pubmed/35359869
http://dx.doi.org/10.3389/fphar.2022.837715
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AT pulimeng pocket2druganencoderdecoderdeepneuralnetworkforthetargetbaseddrugdesign
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