Improved Protein–Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference-Reference-Cited by-同舟云学术

Improved Protein–Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

Published:2021-03-23 Issue:4 Volume:61 Page:1583-1592
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Jones Derek¹^ORCID,Kim Hyojin²^ORCID,Zhang Xiaohua³,Zemla Adam¹,Stevenson Garrett⁴,Bennett W. F. Drew³^ORCID,Kirshner Daniel³,Wong Sergio E.³,Lightstone Felice C.³,Allen Jonathan E.¹

Affiliation:

1. Global Security Computing Applications Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States

2. Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States

3. Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States

4. Computational Engineering Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States

Funder

Lawrence Livermore National Laboratory

American Heart Association

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.0c01306

Reference39 articles.

1. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

2. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

3. PotentialNet for Molecular Property Prediction

4. DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity

5. Protein–Ligand Scoring with Convolutional Neural Networks

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