Ignacio Pickering

We present BitBIRCH-Lean, a fast, memory-efficient implementation of the BitBIRCH algorithm, designed for high-throughput clustering of huge molecular libraries (up to billions of drug-like molecules) on modern scientific workstations.

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.

In this work we introduce TorchANI 2.0, a significantly improved version of the free and open source TorchANI software package for training and evaluation of ANI (ANAKIN-ME) deep learning models. TorchANI 2.0 builds upon the foundation of its predecessor, while addressing its limitations and introducing new features. These changes greatly enhance its extensibility, performance, and suitability as a framework for developing models ready for molecular dynamics applications. These improvements include the introduction of a modular system to add arbitrary pairwise potentials to models, CUDA-accelerated optimization for faster and more memory-efficient calculation of local atomic features, and a batched system for better performance of network ensembles, among others. Our benchmarks demonstrate that TorchANI 2.0 achieves significant speedup over previous versions in both training and inference, and the library enhancements allow users to train physically constrained models that better represent important qualities of chemical systems. We demonstrate this by introducing three new ANI models that incorporate these features and evaluating their capabilities