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.
Highlights
The prediction of physicochemical properties of small, drug-like molecules has been the focus of the Statistical Assessment of the Modeling of Proteins and Ligands series of challenges for several years [1]
A subset of the molecules provided during the SAMPL6 challenge for the prediction of acidity constants [2, 3] was selected by the organizers to challenge the community again with the task to predict their neutral-state partitioning
[3], and the dry and wet n-octanol models under investigation are shown in Fig. 1 and Table 1, for the latter including the optional scaling parameter for the excess chemical potential (“2-par”) besides the partial molar volume (PMV)-only correction (“1-par”)
Summary
The prediction of physicochemical properties of small, drug-like molecules has been the focus of the Statistical Assessment of the Modeling of Proteins and Ligands series of challenges for several years [1]. As in the earlier challenges we here employed the “embedded cluster reference interaction site model” (ECRISM) to characterize the thermodynamics of the solvation process [8] This method combines 3D RISM integral equation theory [9,10,11] with a quantum-chemical (QC) description of the solute to capture electronic solute polarization. We took the sum of the polarized electronic energy and the excess chemical potential as an estimate of the Gibbs energy of the molecule in solution to calculate derived properties such as solvation Gibbs energies (by referencing to a gas phase calculation), acidity constants, partition and distribution coefficients, or tautomer and conformational populations of molecules under ambient and extreme conditions in a variety of solvents [14,15,16,17,18,19,20,21]
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