{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Morado J"],"funding":["AstraZeneca","Engineering and Physical Sciences Research Council"],"pagination":["2810-2827"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10170518"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["63(9)"],"pubmed_abstract":["We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. Th"],"journal":["Journal of chemical information and modeling"],"pubmed_title":["Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins."],"pmcid":["PMC10170518"],"funding_grant_id":["EP/L015722/1","EP/P020275/1"],"pubmed_authors":["Nissink JWM","Skylaris CK","Essex JW","Mortenson PN","Morado J"],"additional_accession":[]},"is_claimable":false,"name":"Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins.","description":"We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. Th","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-20T02:53:38.812Z","creation":"2024-12-03T21:25:38.09Z"},"accession":"S-EPMC10170518","cross_references":{"pubmed":["37071825"],"doi":["10.1021/acs.jcim.2c01510"]}}