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Found 5 papers, 2 papers with code
Navigating protein landscapes with a machine-learned transferable coarse-grained model
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
Machine Learning Small Molecule Properties in Drug Discovery
Overall, this review provides insights into the landscape of ML models for small molecule property predictions in drug discovery.
Top-down machine learning of coarse-grained protein force-fields
Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended timescales.
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems.
Coarse Graining Molecular Dynamics with Graph Neural Networks
5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space.
