Torsionator: Dihedral Parameterization and Conformer Search Made Easy

Abstract

Abstract Neural network potentials (NNPs), trained on quantum mechanical (QM) data, can deliver near QM-level accuracy while being much faster than QM calculations. In this work, we introduce a new software tool, Torsionator, which currently supports MACE, OBIWAN, and UMA NNPs for energy minimization of small molecules. Torsionator can analyze a set of selected conformers to parameterize the dihedral angles. These dihedral parameters can then be used for molecular mechanics calculations and simulations. We showcase Torsionator by performing dihedral scanning and parameterization of two representative antiviral compounds: favipiravir defluoro analog T1105 and emivirine. To thoroughly evaluate its robustness and general applicability, we benchmarked Torsionator using MACE on the TorsionNet500 dataset, comprising 500 chemically diverse small molecules with reference QM torsional profiles. These results indicate Torsionator as a practical, efficient, and scalable software tool for dihedral parameterization, enabling its routine integration into molecular simulation workflows.