TorchANI-Amber: Bridging neural network potentials and classical biomolecular simulations

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Abstract
In this work we introduce TorchANI-Amber, an interface for routine molecular dy- namics simulations of biomolecular systems using ANI-style machine learning poten- tials. TochANI-Amber incorporates the ANI neural network potentials into the Amber software suite, supporting Amber’s two engines: sander and pmemd. In addition to implementing all published ANI models, the interface is extensible to other energy predicting potentials through a simple mechanism requiring no knowledge of Amber’s codebase. To illustrate this versatility, we implement extensions to the AIMNet2 and Nutmeg potentials. The interface is integrated with Amber’s neighborlists, and it also supports an optimized CUDA implementation for computing the ANI models’ feature vectors, enabling simulations of systems with hundreds of thousands of atoms at the neural network’s level of theory (approaching DFT accuracy). The interface is designed so that all amber capabilities can be used with ANI potentials instead of force fields. To evaluate the energy conservation, stability, and performance of the ANI potentials as used through the interface, we run MD simulations on different biomolecular systems, including ubiquitin and Trp-cage proteins in explicit solvent. Additionally, we demon- strate the use of these potentials in the context of enhanced sampling techniques, such as temperature replica-exchange molecular dynamics.

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