Multiple linear regression models for predicting the n‑octanol/water partition coefficients in the SAMPL7 blind challenge

• Published in: Journal of computer-aided molecular design Vol. 35; no. 8; pp. 923 - 931
• Main Authors: Lopez, Kenneth, Pinheiro, Silvana, Zamora, William J.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-08-2021; Springer Nature B.V
• Subjects: 1-Octanol - chemistry; Animal Anatomy; Antibiotics; Chemistry; Chemistry and Materials Science; Coefficients; Computer Applications in Chemistry; Computer Simulation; Functional groups; Histology; Linear Models; Models, Chemical; Morphology; Organic chemistry; Physical Chemistry; Physicochemical properties; Quantum Theory; Regression analysis; Regression models; Root-mean-square errors; Solubility; Sulfonamides; Thermodynamics; Water - chemistry; Octanol/water partition coefficients; sulfonamides; Multiple linear regression; SAMPL7 blind challenge; Empirical methods; N-sulfonamides; n-Octanol/water partition coefficients
• ISSN: 0920-654X; 1573-4951
• DOI: 10.1007/s10822-021-00409-2

A multiple linear regression model called MLR-3 is used for predicting the experimental
n
-octanol/water partition coefficient (log
P
N
) of 22
N
-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as “TFE-MLR”, presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log
P
units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the
n
-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75% of the ranked submissions achieved root-mean-square errors < 1 log
P
units, support the suitability of these strategies for obtaining accurate and fast predictions of physicochemical properties as partition coefficients of bioorganic compounds.