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Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
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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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Cited by 3 Pith papers
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A potassium ion channel simulated with a universal neural network potential
Simulating the KcsA selectivity filter with the Orb-D3 neural network potential reveals a T75 hydroxyl-water hydrogen bond that stabilizes water in the filter and enables soft knock-on potassium transport with a condu...
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Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations
An electrostatic-embedding MLIP/MM scheme with machine-learned RESP charges improves relative binding free energy predictions on TYK2 but not on four other benchmark targets.
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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.
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