Prediction of the n-octanol/water partition coefficients in the SAMPL6 blind challenge from MST continuum solvation calculations

Abstract

The IEFPCM/MST continuum solvation model is used for the blind prediction of noctanol/water partition of a set of 11 fragment-like small molecules within the SAMPL6 Part II Partition Coefficient Challenge. The partition coefficient of the neutral species (log P) was determined using an extended parametrization of the B3LYP/6-31G(d) version of the Miertus-Scrocco-Tomasi continuum solvation model in n-octanol. Comparison with the experimental data provided for partition coefficients yielded a root-mean square error (rmse) of 0.78 (log P units), which agrees with the accuracy reported for our method (rmse = 0.80) for nitrogencontaining heterocyclic compounds. Out of the 91 sets of log P values submitted by the participants, our submission is within those with an rmse < 1 and among the four best ranked physical methods. The largest errors involve three compounds: two with the largest positive deviations (SM13 and SM08), and one with the largest negative deviations (SM15). Here we report the potentiometric determination of the log P for SM13, leading to a value of 3.62 ± 0.02, which is in better agreement with most empirical predictions than the experimental value reported in SAMPL6. In addition, further inclusion of several conformations for SM08 significantly improved our results. Inclusion of these refinements led to an overall error of 0.51 (log P units), which supports the reliability of the IEFPCM/MST model for predicting the partitioning of neutral compounds.

Prediction of Toluene/Water Partition Coefficient in the SAMPL9 Blind Challenge: Assessment of Machine Learning and IEF-PCM/MST Continuum Solvation Models

“…9). The results pointed out that reliable estimates of the partition coefficient are obtained in the two solvents (RSME < 1 logP units), which agree with those previously reported for the IEF-PCM/MST method in the noctanol/water system applied to nitrogen-containing aromatic compounds (RSME = 0.8 logP units), drug-like compounds (RSME = 1.1 logP units) [81], fragment-like small molecules in the SAMPL6 logP challenge (RSME = 0.8 logP units) [82], and sulfonamide-containing compounds in the SAMPL7 logP contest (RSME = 1.0 logP units) [83]. The similar performance for the solvation in distinct solvents is likely determined by the similar number of experimental data used in the parameterization of the model (91, 72, and 58 for toluene, benzene, and n-octanol [63], respectively).…”

Prediction of n-octanol/water partition coefficients and acidity constants (pKa) in the SAMPL7 blind challenge with the IEFPCM-MST model

“…Here, we report the results obtained for predicting the log P and pK a for a group of sulfonamide-containing compounds. The results are discussed in light of the experimental data provided by the organizers of SAMPL7 [21] and the theoretical estimates reported by others groups, as well as with the IEFPCM/MST results obtained in previous editions of this contest [22,23].…”

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

“…Previous SAMPL challenges have looked at the prediction of solvation free energies [8][9][10][11][12], guest-host [13][14][15][16][17][18][19] and protein-ligand binding affinities [20][21][22][23][24][25][26], pK a [27][28][29][30][31][32][33], distribution coefficients [34][35][36][37], and partition coefficients [38][39][40][41]. These challenges have helped uncover sources of error, pinpoint the reasons various methods performed poorly or well and their strengths and weaknesses, and facilitate (1) log P = log 10 K ow = log 10 [unionized solute] octanol [unionized solute] water dissemination of lessons learned after each challenge ends, ultimately leading to improved methods and algorithms.…”