Water Network-Augmented Two-State Model for Protein–Ligand Binding Affinity Prediction-Reference-Cited by-同舟云学术

Water Network-Augmented Two-State Model for Protein–Ligand Binding Affinity Prediction

Published:2023-07-11 Issue: Volume: Page:
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Qu Xiaoyang¹,Dong Lina¹,Luo Ding¹,Si Yubing²^ORCID,Wang Binju¹³^ORCID

Affiliation:

1. State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China

2. College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China

3. Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, P. R. China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00567

Reference68 articles.

1. Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

2. DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method

3. Classification of Current Scoring Functions

4. Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data

5. From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

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