On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction-Reference-Cited by-同舟云学术

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On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction

Published:2022-09-13 Issue:18 Volume:62 Page:4295-4299
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Born Jannis¹²^ORCID,Shoshan Yoel³,Huynh Tien⁴,Cornell Wendy D.⁴,Martin Eric J.⁵^ORCID,Manica Matteo¹^ORCID

Affiliation:

1. Accelerated Discovery, IBM Research Europe, 8803 Rüschlikon, Switzerland

2. Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland

3. IBM Research Haifa, Q288+48 Haifa 31905, Israel

4. IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States

5. Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States

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.2c00840

Reference15 articles.

1. Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives

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

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

4. MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities

5. DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks

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