RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks-Reference-Cited by-同舟云学术

RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks

Published:2020-05-11 Issue:6 Volume:60 Page:2791-2802
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Hassan-Harrirou Hussein¹,Zhang Ce¹,Lemmin Thomas¹²^ORCID

Affiliation:

1. DS3Lab, System Group, Department of Computer Sciences, ETH Zurich, CH-8092 Zurich, Switzerland

2. Institute of Medical Virology, University of Zurich (UZH), CH-8057 Zurich, Switzerland

Funder

Schweizerischer Nationalfonds zur F??rderung der Wissenschaftlichen Forschung

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.0c00075

Reference44 articles.

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2. Yaseen, A.; Abbasi, W. A. Protein Binding Affinity Prediction Using Support Vector Regression and Interfecial Features. In 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST); IEEE, 2018; pp 194–198.

3. MoleculeNet: a benchmark for molecular machine learning

4. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

5. Pred-binding: large-scale protein–ligand binding affinity prediction

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