ArticleMolecules (Basel, Switzerland)2022
An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Assessing conformation validity and rationality of deep learning-generated 3D molecules.Nature communications · 2026Article
- Incorporating Neural Networks into the AMOEBA Polarizable Force Field.The journal of physical chemistry. B · 2024Article
- Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.Journal of chemical information and modeling · 2023Article
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Authors and funding
4 authors.
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Abstract
Accurate conformational energetics of molecules are of great significance to understand maby chemical properties. They are also fundamental for high-quality parameterization of force fields. Traditionally, accurate conformational profiles are obtained with density functional theory (DFT) methods. However, obtaining a reliable energy profile can be time-consuming when the molecular sizes are relatively large or when there are many molecules of interest. Furthermore, incorporation of data-driven deep learning methods into force field development has great requirements for high-quality geometry and energy data. To this end, we compared several possible alternatives to the traditional DFT methods for conformational scans, including the semi-empirical method GFN2-xTB and the neural network potential ANI-2x. It was found that a sequential protocol of geometry optimization with the semi-empirical method and single-point energy calculation with high-level DFT methods can provide satisfactory conformational energy profiles hundreds of times faster in terms of optimization.
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