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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-8962739 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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