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06/20/2023
Top-down machine learning of coarse-grained protein force-fields
Developing accurate and efficient coarse-grained representations of prot...
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06/10/2023
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
The development of efficient machine learning models for molecular syste...
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02/20/2023
Binding-and-folding recognition of an intrinsically disordered protein using online learning molecular dynamics
Intrinsically disordered proteins participate in many biological process...
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12/14/2022
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
A generalized understanding of protein dynamics is an unsolved scientifi...
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09/21/2022
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Machine learning potentials are an important tool for molecular simulati...
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02/05/2022
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials
The prediction of quantum mechanical properties is historically plagued ...
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01/20/2022
NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics
Parametric and non-parametric machine learning potentials have emerged r...
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12/22/2020
TorchMD: A deep learning framework for molecular simulations
Molecular dynamics simulations provide a mechanistic description of mole...
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07/22/2020
Coarse Graining Molecular Dynamics with Graph Neural Networks
Coarse graining enables the investigation of molecular dynamics for larg...
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07/07/2020
Guided Exploration with Proximal Policy Optimization using a Single Demonstration
Solving sparse reward tasks through exploration is one of the major chal...
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07/06/2020
NAPPO: Modular and scalable reinforcement learning in pytorch
Reinforcement learning (RL) has been very successful in recent years but...
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11/22/2019
Machine learning for protein folding and dynamics
Many aspects of the study of protein folding and dynamics have been affe...
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10/29/2017
