Employing artificial neural networks for the assessment of the aqueous solubility of drug –like substances

Abstract

This research has advanced Quantitative Structure-Property Relationship (QSPR)
models for predicting the aqueous solubility of drug-like substances. By integrating
multivariate regression and neural network techniques, the study utilized the backward
algorithm to strategically select 2D and 3D molecular descriptors, resulting in the development
of an optimal QSPRMLR model with k = 23. The artificial neural network regression model
(QSPRANN), derived from selected descriptors of the multivariable linear regression model
(QSPRMLR), demonstrated enhanced predictive capabilities for logS values in both validation
and prediction groups, yielding SE values of 0.786 and 0.808, respectively. The QSPRANN
significantly improved the overall predictability of the multivariate regression model. Statistical
assessments of the QSPRANN model revealed SE = 0.699, R2train = 0.918, and Q2v = 0.878.
The predicted logS values from the QSPRANN model align well with experimental data,
confirming the reliability and accuracy of the developed model.