Mayer-Homology Learning Prediction of Protein-Ligand Binding Affinities is a research paper published in Journal of Computational Biophysics and Chemistry (2024). On theSindex it has a DataRank of 0. It has been cited 11 times.
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Foundation for the National Institutes of Health
Grant: R01GM126189
Foundation for the National Institutes of Health
Grant: R01AI164266
Foundation for the National Institutes of Health
Grant: R35GM148196
National Science Foundation
Grant: DMS-2052983
National Science Foundation
Grant: DMS-1761320
National Science Foundation
Grant: DMS-2245903
National Science Foundation
Grant: IIS-1900473
National Aeronautics and Space Administration
Grant: 80NSSC21M0023
Bristol-Myers Squibb
Grant: 65109
National Science Foundation
Grant: 2052983
Collaborative Research: Integrating Algebraic Topology, Graph Theory, and Multiscale Analysis for Learning Complex and Diverse Datasets
National Science Foundation
Grant: 1761320
Kinetics-Driven Drug Discovery Using Persistent Homology, Rare-Event Molecular Dynamics and Experimental Data
National Institutes of Health
Grant: 5R35GM148196-02
Discovery-Driven Mathematics and Artificial Intelligence for Biosciences and Drug Discovery
National Science Foundation
Grant: 1900473
III: Medium: De Rham-Hodge theory modeling and learning of biomolecular data
National Science Foundation
Grant: 2245903
DMS/NIGMS 1: Data-driven Ricci curvatures and spectral graph for machine learning and adaptive virtual screening
National Institutes of Health
Grant: 1R01AI164266-01A1
AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies
National Institutes of Health
Grant: 5R01GM126189-03
Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impacts
Pfizer
Michigan State University Foundation
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