期刊
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
卷 34, 期 5, 页码 485-493出版社
SPRINGER
DOI: 10.1007/s10822-020-00286-1
关键词
Partition coefficient; logP; SAMPL; SMD; Water-octanol; DFT; Implicit solvation
资金
- Intermural Research Program of the National Heart, Lung, and Blood Institute of the National Institutes of Health
Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water-octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water-octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs: DYXBT, O7DJK, and AHMTF.
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