A TensorNet-based neural network potential (AceFF 1.0) in an NNP/MM scheme achieves RBFE accuracy close to OPLS4, exceeding GAFF2 and ANI-2x on most JACS benchmark targets, at a 2 fs timestep.
Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
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abstract
Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simulating complex molecular systems. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields.
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physics.chem-ph 1years
2025 1verdicts
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QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
A TensorNet-based neural network potential (AceFF 1.0) in an NNP/MM scheme achieves RBFE accuracy close to OPLS4, exceeding GAFF2 and ANI-2x on most JACS benchmark targets, at a 2 fs timestep.