ArticleJournal of chemical information and modeling2023
Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- Effects of Substituents on 9‑Fluorenone: Spectroscopic, Hirshfeld, Optical, NLO, IFCT, and CTM Properties.ACS omega · 2026Article
- Unraveling Torsional Preferences: Comparative Analysis of Torsion Motif Torsional-Angle Distributions across Different Environments.Journal of chemical information and modeling · 2025Article
- Benchmarking Structures and UV-Vis Spectra of Iron Complexes Against Experimental Data.The journal of physical chemistry. A · 2025Article
- Discriminating High from Low Energy Conformers of Druglike Molecules: An Assessment of Machine Learning Potentials and Quantum Chemical Methods.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- Conformer Generation for Structure-Based Drug Design: How Many and How Good?Journal of chemical information and modeling · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
We performed exhaustive torsion sampling on more than 3 million compounds using the GFN2-xTB method and performed a comparison of experimental crystallographic and gas-phase conformers. Many conformer sampling methods derive torsional angle distributions from experimental crystallographic data, limiting the torsion preferences to molecules that must be stable, synthetically accessible, and able to be crystallized. In this work, we evaluate the differences in torsional preferences of experimental crystallographic geometries and gas-phase computed conformers from a broad selection of compounds to determine whether torsional angle distributions obtained from semiempirical methods are suitable priors for conformer sampling. We find that differences in torsion preferences can be mostly attributed to a lack of available experimental crystallographic data with small deviations derived from gas-phase geometry differences. GFN2 demonstrates the ability to provide accurate and reliable torsional preferences that can provide a basis for new methods free from the limitations of experimental data collection. We provide Gaussian-based fits and sampling distributions suitable for torsion sampling and propose an alternative to the widely used "experimental torsion and knowledge distance geometry" (ETKDG) method using quantum torsion-derived distance geometry (QTDG) methods.
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Registered trials
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