ArticleJournal of chemical information and modeling2025
DeepConf: Leveraging ANI-ML Potentials for Exploring Local Minima with Application to Bioactive Conformations.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- A conformational benchmark for optical property prediction with solvent-aware graph neural networks.Communications chemistry · 2026Article
- Data-Efficient Equivariant NNPs Enable DFT-Accurate Simulations and Implicit Solvation Free Energies.The journal of physical chemistry. B · 2025Article
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Authors and funding
4 authors.
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Abstract
Here, we introduce a low energy conformer generation algorithm using ANI-ML potentials at the DFT accuracy and benchmark in reproducing bioactive conformations. We show that the method is efficient when the initial starting structure is far from equilibrium, when the ML potentials are stuck in nonsmooth regions, and when the quality of the conformers in a less conformer size is demanded. We specifically focus on conformations due to rotations around the single bonds. For the first time, we assess the performance of ANI-ML potentials using our conformer generation algorithm, DeepConf, in addition to previously reported Auto3D (
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