Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases-Reference-Cited by-同舟云学术

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Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases

Published:2024-05-16 Issue:10 Volume:64 Page:4009-4020
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Backenköhler Michael¹^ORCID,Groß Joschka²,Wolf Verena²,Volkamer Andrea¹³^ORCID

Affiliation:

1. Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken 66123, Germany

2. Modeling and Simulation, Saarland University, Saarbrücken 66123, Germany

3. Structural Bioinformatics and in Silico Toxicology Institute of Physiology, Universitätsmedizin Berlin, Berlin 10117, Germany

Funder

Universit?t des Saarlandes

Berlin Institute of Health

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00055

Reference69 articles.

1. Heid, E.; Greenman, K. P.; Chung, Y.; Li, S.C.; Graff, D. E.; Vermeire, F. H.; Wu, H.; Green, W. H.; McGill, C. J. Chemprop: Machine Learning Package for Chemical Property Prediction. 2023, ChemRxiv. 10.26434/chemrxiv-2023-3zcfl.

2. TorchMD: A Deep Learning Framework for Molecular Simulations

3. Equivariant Graph Neural Networks for Toxicity Prediction

4. Deep graph learning in molecular docking: Advances and opportunities

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lessons learned during the journey of data: from experiment to model for predicting kinase affinity, selectivity, polypharmacology, and resistance;2024-09-10

2. PILOT: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via importance sampling;Chemical Science;2024

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