Huang Group at UW-Madison
@xuhuihuangThe Huang Group at Department of Chemistry, University of Wisconsin-Madison
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21
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FeatureDock is a protein-ligand docking software guided by physicochemical feature-based local environment learning using Transformer
Tutorials for non-Markovian dynamic models (qMSM and IGME) to study protein dynamics.
TS-DAR identifies transition states of protein conformational changes from MD simulations using hyperspherical embeddings in the latent space.
Jupyter Notebook Tutorials for Machine Learning in Chemistry (MLChem)
GraphVAMPnets combines graph neural networks with variational approach for Markovian process (VAMP) theory to identify the slow collective variables of the self-assembly processes.
Integrative Generalized Master Equation (IGME): A Theory to Study Long-timescale Biomolecular Dynamics via the Integrals of Memory Kernels
MEMnets identifies slow CVs of biomolecular dynamics by minimizing their time-integrated memory kernels.
TS-DAR tutorials. The TS-DAR code is here: https://github.com/xuhuihuang/ts-dar
A Continuous-Space Analytical Framework for Committor Functions from Molecular Dynamics
Mechanism of SMG9 Substrate Recognition by O-GlcNAc Transferase Revealed by Markov State Models and Transition State Analysis
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