DLSSAffinity: protein–ligand binding affinity prediction <i>via</i> a deep learning model-Reference-Cited by-同舟云学术

DLSSAffinity: protein–ligand binding affinity prediction via a deep learning model

Published:2022 Issue:17 Volume:24 Page:10124-10133
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Wang Huiwen¹^ORCID,Liu Haoquan²,Ning Shangbo²,Zeng Chengwei²,Zhao Yunjie²^ORCID

Affiliation:

1. School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China

2. Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China

Abstract

We propose a novel deep learning-based approach, DLSSAffinity, to accurately predict protein–ligand binding affinity. We show that combining global sequence and local structure information as the input features of a deep learning model can improve the prediction accuracy.

Funder

National Natural Science Foundation of China

Central China Normal University

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D1CP05558E

Reference48 articles.

1. Calculation of Protein-Ligand Binding Affinities

2. OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction