Authors: | Tielker, Nicolas Tomazic, Daniel Eberlein, Lukas Güssregen, Stefan Kast, Stefan M. |
Title: | The SAMPL6 challenge on predicting octanol–water partition coefficients from EC-RISM theory |
Language (ISO): | en |
Abstract: | Results are reported for octanol–water partition coefficients (log P) of the neutral states of drug-like molecules provided during the SAMPL6 (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenge from applying the “embedded cluster reference interaction site model” (EC-RISM) as a solvation model for quantum-chemical calculations. Following the strategy outlined during earlier SAMPL challenges we first train 1- and 2-parameter water-free (“dry”) and water-saturated (“wet”) models for n-octanol solvation Gibbs energies with respect to experimental values from the “Minnesota Solvation Database” (MNSOL), yielding a root mean square error (RMSE) of 1.5 kcal mol−1 for the best-performing 2-parameter wet model, while the optimal water model developed for the pKa part of the SAMPL6 challenge is kept unchanged (RMSE 1.6 kcal mol−1 for neutral compounds from a model trained on both neutral and ionic species). Applying these models to the blind prediction set yields a log P RMSE of less than 0.5 for our best model (2-parameters, wet). Further analysis of our results reveals that a single compound is responsible for most of the error, SM15, without which the RMSE drops to 0.2. Since this is the only compound in the challenge dataset with a hydroxyl group we investigate other alcohols for which Gibbs energy of solvation data for both water and n-octanol are available in the MNSOL database to demonstrate a systematic cause of error and to discuss strategies for improvement. |
Subject Headings: | SAMPL6 Solvation model Quantum chemistry Integral equation theory EC-RISM log P |
Subject Headings (RSWK): | Solvatation Quantenchemie Integralgleichung Octanole Wasser Verteilungskoeffizient |
URI: | http://hdl.handle.net/2003/40106 http://dx.doi.org/10.17877/DE290R-21983 |
Issue Date: | 2020-01-24 |
Rights link: | https://creativecommons.org/licenses/by/4.0/ |
Appears in Collections: | Physikalische Chemie |
Files in This Item:
File | Description | Size | Format | |
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Tielker2020_Article_TheSAMPL6ChallengeOnPredicting.pdf | 1.36 MB | Adobe PDF | View/Open |
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