ArticleJournal of computer-aided molecular design2020
Quantum chemical predictions of water-octanol partition coefficients applied to the SAMPL6 logP blind challenge.
Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Predicting Chromatographic Retention Times from Quantum-Chemical Solvation Free Energies: A Pilot Study of Oxysterols.The journal of physical chemistry. A · 2026Article
- Leveraging Molecular Mechanics With the uESE Continuum Solvation Model for Efficient Solvation Free Energy Prediction: Impact of Conformation and Extensive Validation.Journal of computational chemistry · 2025Article
- Integrating multiscale and machine learning approaches towards the SAMPL9 logPhysical chemistry chemical physics : PCCP · 2024Article
- Effective and Selective Ru(II)-Arene Complexes Containing 4,4'-Substituted 2,2' Bipyridine Ligands Targeting Human Urinary Bladder Cancer Cells.International journal of molecular sciences · 2023Article
- SAMPL7 blind challenge: quantum-mechanical prediction of partition coefficients and acid dissociation constants for small drug-like molecules.Journal of computer-aided molecular design · 2021Article
- Energy-entropy prediction of octanol-water logP of SAMPL7 N-acyl sulfonamide bioisosters.Journal of computer-aided molecular design · 2021Article
- Evaluation of log P, pKJournal of computer-aided molecular design · 2021Article
- Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge.Journal of computer-aided molecular design · 2020Article
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2 authors.
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Abstract
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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