ArticleJournal of chemical theory and computation2025
Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.
Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Chemically tuned radiolabeling and membrane affinity in Drug-Functionalized carbon nanotube nanocarriers.Scientific reports · 2025Article
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- 3D structure and stability prediction of DNA with multi-way junctions in ionic solutions.PLoS computational biology · 2025Article
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- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Small molecule empirical force fields (FFs), including the CHARMM General Force Field (CGenFF), are designed to have wide coverage of organic molecules and to rapidly assign parameters to molecules not explicitly included in the FF. Assignment of parameters to new molecules in CGenFF is based on a trained bond-angle-dihedral charge increment linear interpolation scheme for the partial atomic charges along with bonded parameters assigned based on analogy using a rules-based penalty score scheme associated with atom types and chemical connectivity. Accordingly, the accuracy of CGenFF is related to the extent of the training set of available parameters. In the present study that training set is extended by 1390 molecules selected to represent connectivities new to CGenFF training compounds. Quantum mechanical (QM) data for optimized geometries, bond, valence angle, and dihedral angle potential energy scans, interactions with water, molecular dipole moments, and electrostatic potentials were used as target data. The resultant bonded parameters and partial atomic charges were used to train a new version of the CGenFF program, v5.0, which was used to generate parameters for a validation set of molecules, including drug-like molecules approved by the FDA, which were then benchmarked against both experimental and QM data. CGenFF v5.0 shows overall improvements with respect to QM intramolecular geometries, vibrations, dihedral potential energy scans, dipole moments and interactions with water. Tests of pure solvent properties of 216 molecules show small improvements versus the previous release of CGenFF v2.5.1 reflecting the high quality of the Lennard-Jones parameters that were explicitly optimized during the initial optimization of both the CGenFF and the CHARMM36 force field. CGenFF v5.0 represents an improvement that is anticipated to more accurately model intramolecular geometries and strain energies as well as noncovalent interactions of drug-like and other organic molecules.
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