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Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods
Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713720/ https://www.ncbi.nlm.nih.gov/pubmed/31507420 http://dx.doi.org/10.3389/fphar.2019.00924 |
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author | Wang, Dingyan Cui, Chen Ding, Xiaoyu Xiong, Zhaoping Zheng, Mingyue Luo, Xiaomin Jiang, Hualiang Chen, Kaixian |
author_facet | Wang, Dingyan Cui, Chen Ding, Xiaoyu Xiong, Zhaoping Zheng, Mingyue Luo, Xiaomin Jiang, Hualiang Chen, Kaixian |
author_sort | Wang, Dingyan |
collection | PubMed |
description | Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effectively construct TSSFs will be of great value to drug design and discovery. In this work, we proposed a deep learning–based model named DeepScore to achieve this goal. DeepScore adopted the form of PMF scoring function to calculate protein–ligand binding affinity. However, different from PMF scoring function, in DeepScore, the score for each protein–ligand atom pair was calculated using a feedforward neural network. Our model significantly outperformed Glide Gscore on validation data set DUD-E. The average ROC-AUC on 102 targets was 0.98. We also combined Gscore and DeepScore together using a consensus method and put forward a consensus model named DeepScoreCS. The comparison results showed that DeepScore outperformed other machine learning–based TSSFs building methods. Furthermore, we presented a strategy to visualize the prediction of DeepScore. All of these results clearly demonstrated that DeepScore would be a useful model in constructing TSSFs and represented a novel way incorporating deep learning and drug design. |
format | Online Article Text |
id | pubmed-6713720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67137202019-09-10 Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods Wang, Dingyan Cui, Chen Ding, Xiaoyu Xiong, Zhaoping Zheng, Mingyue Luo, Xiaomin Jiang, Hualiang Chen, Kaixian Front Pharmacol Pharmacology Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effectively construct TSSFs will be of great value to drug design and discovery. In this work, we proposed a deep learning–based model named DeepScore to achieve this goal. DeepScore adopted the form of PMF scoring function to calculate protein–ligand binding affinity. However, different from PMF scoring function, in DeepScore, the score for each protein–ligand atom pair was calculated using a feedforward neural network. Our model significantly outperformed Glide Gscore on validation data set DUD-E. The average ROC-AUC on 102 targets was 0.98. We also combined Gscore and DeepScore together using a consensus method and put forward a consensus model named DeepScoreCS. The comparison results showed that DeepScore outperformed other machine learning–based TSSFs building methods. Furthermore, we presented a strategy to visualize the prediction of DeepScore. All of these results clearly demonstrated that DeepScore would be a useful model in constructing TSSFs and represented a novel way incorporating deep learning and drug design. Frontiers Media S.A. 2019-08-22 /pmc/articles/PMC6713720/ /pubmed/31507420 http://dx.doi.org/10.3389/fphar.2019.00924 Text en Copyright © 2019 Wang, Cui, Ding, Xiong, Zheng, Luo, Jiang and Chen http://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 Wang, Dingyan Cui, Chen Ding, Xiaoyu Xiong, Zhaoping Zheng, Mingyue Luo, Xiaomin Jiang, Hualiang Chen, Kaixian Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods |
title | Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods |
title_full | Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods |
title_fullStr | Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods |
title_full_unstemmed | Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods |
title_short | Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods |
title_sort | improving the virtual screening ability of target-specific scoring functions using deep learning methods |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713720/ https://www.ncbi.nlm.nih.gov/pubmed/31507420 http://dx.doi.org/10.3389/fphar.2019.00924 |
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