Abstract：
:Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent on three-dimensional structures or even conformer ensembles, the majority of models are based on descriptors derived from two-dimensional structures. Here we present results from a thorough benchmark study of force field, semiempirical, and density functional methods for the calculation of conformer energies in the gas phase and water solvation as a foundation for the correct identification of relevant low-energy conformers. We find that the tight-binding ansatz GFN-xTB shows the lowest error metrics and highest correlation to the benchmark PBE0-D3(BJ)/def2-TZVP in the gas phase for the computationally fast methods and that in solvent OPLS3 becomes comparable in performance. MMFF94, AM1, and DFTB+ perform worse, whereas the performance-optimized but far more expensive functional PBEh-3c yields energies almost perfectly correlated to the benchmark and should be used whenever affordable. On the basis of our findings, we have implemented a reliable and fast protocol for the identification of low-energy conformers of drug-like molecules in water that can be used for the quantification of strain energy and entropy contributions to target binding as well as for the derivation of conformer-ensemble-dependent molecular descriptors.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Cavasin AT,Hillisch A,Uellendahl F,Schneckener S,Göller AHdoi
10.1021/acs.jcim.8b00151subject
Has Abstractpub_date
2018-05-29 00:00:00pages
1005-1020issue
5eissn
1549-9596issn
1549-960Xjournal_volume
58pub_type
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pub_type： 杂志文章
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journal_title：Journal of chemical information and modeling
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