Physics-Guided Deep Generative Model For New Ligand Discovery
Author:
Affiliation:
1. Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States
Funder
NSF (National Science Foundation)
NIH (National Institutes of Health)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3584371.3613067
Reference30 articles.
1. A Physics-Guided Neural Network for Predicting Protein–Ligand Binding Free Energy: From Host–Guest Systems to the PDBbind Database
2. David A Case etal 2021. Amber 2021. University of California San Francisco. David A Case et al. 2021. Amber 2021. University of California San Francisco.
3. Peter Ertl Richard Lewis Eric Martin and Valery Polyakov. 2017. In silico generation of novel drug-like chemical matter using the lstm neural network. arXiv preprint arXiv:1712.07449. Peter Ertl Richard Lewis Eric Martin and Valery Polyakov. 2017. In silico generation of novel drug-like chemical matter using the lstm neural network. arXiv preprint arXiv:1712.07449.
4. Grid-Based Surface Generalized Born Model for Calculation of Electrostatic Binding Free Energies
5. Multidimensional Global Optimization and Robustness Analysis in the Context of Protein–Ligand Binding
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