Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning-Reference-Cited by-同舟云学术

Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning

Published:2024 Issue:7 Volume:14 Page:4492-4502
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Isert Clemens¹^ORCID,Atz Kenneth¹^ORCID,Riniker Sereina¹^ORCID,Schneider Gisbert¹^ORCID

Affiliation:

1. ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland

Abstract

A deep learning approach centered on electron density is suggested for predicting the binding affility between proteins and ligands. The approach is thoroughly assessed using various pertinent benchmarks.

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RA/D3RA08650J

Reference117 articles.

1. T.Steinbrecher , in Free Energy Calculations in Drug Lead Optimization , John Wiley & Sons, Ltd , 2012, ch . 11 , pp. 207–236

2. On the Frustration to Predict Binding Affinities from Protein–Ligand Structures with Deep Neural Networks

3. S.Li , J.Zhou , T.Xu , L.Huang , F.Wang , H.Xiong , W.Huang , D.Dou and H.Xiong , Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , 2021 , pp. 975–985

4. PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions

5. Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)

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