Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein–Ligand Binding Affinity Prediction is a research paper published in Journal of Chemical Theory and Computation (2025). On theSindex it has a DataRank of 0. It has been cited 4 times.
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Bristol-Myers Squibb
Grant: 65109
National Institutes of Health
Grant: R01AI164266
National Institutes of Health
Grant: R01GM126189
National Science Foundation
Grant: DMS-2052983
National Science Foundation
Grant: IIS-1900473
National Science Foundation
Grant: 1900473
III: Medium: De Rham-Hodge theory modeling and learning of biomolecular data
National Institutes of Health
Grant: 1R01AI164266-01A1
AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies
National Science Foundation
Grant: 2052983
Collaborative Research: Integrating Algebraic Topology, Graph Theory, and Multiscale Analysis for Learning Complex and Diverse Datasets
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
Michigan State University
FWCI
2.74
Citation Percentile
0.9%
Citation Trend
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Sustainable Development Goals