Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies is a research paper published in Chemical Science (2021). On theSindex it has a DataRank of 0. It has been cited 118 times.
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National Aeronautics and Space Administration
Grant: 80NSSC21M0023
National Institutes of Health
Grant: GM126189
National Science Foundation
Grant: DMS-2052983
National Science Foundation
Grant: DMS-1761320
National Science Foundation
Grant: IIS-1900473
George Mason University
Grant: PD45722
Bristol-Myers Squibb
Grant: 65109
NIGMS NIH HHS
Grant: R01 GM126189
NIAID NIH HHS
Grant: R01 AI164266
National Science Foundation
Grant: 1721024
Geometric and Topological Modeling and Computation of Biomolecular Structure, Function, and Dynamics
National Science Foundation
Grant: 1900473
III: Medium: De Rham-Hodge theory modeling and learning of biomolecular data
National Science Foundation
Grant: 1761320
Kinetics-Driven Drug Discovery Using Persistent Homology, Rare-Event Molecular Dynamics and Experimental Data
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
National Science Foundation
Grant: 2052983
Collaborative Research: Integrating Algebraic Topology, Graph Theory, and Multiscale Analysis for Learning Complex and Diverse Datasets
Pfizer
Michigan Economic Development Corporation
FWCI
6.75
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals
Additional file 1 of AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning
Additional file 1 of AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning