Torsionator: Dihedral Parameterization and Conformer Search Made Easy.
- 1Laboratory of Molecular Modelling & Drug Discovery, Fondazione Istituto Italiano di Tecnologia, via Morego 30, Genoa16163, Italy.
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.
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BibTeX
@article{brosio2026torsionator,
title = {{Torsionator: Dihedral Parameterization and Conformer Search Made Easy.}},
author = {Giorgia Brosio and Alice Triveri and Pietro Vidossich and Sergio Decherchi and Marco De Vivo},
journal = {Journal of chemical theory and computation},
year = {2026},
month = oct,
doi = {10.1021/acs.jctc.6c01006},
url = {https://europepmc.org/article/MED/42917256},
note = {CC BY},
}
RIS
TY - JOUR
TI - Torsionator: Dihedral Parameterization and Conformer Search Made Easy.
AU - Giorgia Brosio
AU - Alice Triveri
AU - Pietro Vidossich
AU - Sergio Decherchi
AU - Marco De Vivo
PY - 2026
DA - 2026/10/01
JO - Journal of chemical theory and computation
DO - 10.1021/acs.jctc.6c01006
UR - https://europepmc.org/article/MED/42917256
AB - 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.
SN - 1549-9618
SN - 1549-9626
ER -