Monte Carlo optimization-based QSPR modeling of molar refractivity: Descriptor stability and applicability domain analysis.
An additive fragment-based QSPR model for molar refractivity (MR) was developed using Monte Carlo optimization within the CORAL framework. Molecular structure was encoded using fragment-level attributes, and their contributions were statistically optimized across multiple independent training-validation splits. The resulting linear models retain the additive character of classical fragment-constant approaches while introducing data-driven weight optimization under explicit statistical control. Across three independent splits, the developed QSPR models exhibit consistently high coefficients of determination in both the training and external validation sets, with validation R2 values ranging from approximately 0.86 to 0.89. Root-mean-square errors remained consistent across splits, indicating reproducible behavior of the optimized descriptors. An applicability domain (AD) was defined using statistical defect metrics derived from descriptor distributions. Across independent splits, the applicability domain consistently identified a minority of compounds with rare or unevenly represented SMILES attributes; these statistically under-supported compounds were associated with a modest increase in prediction error, indicating that the defect-based domain reflects descriptor representativeness rather than acting as a strict error filter. The study presents Monte Carlo-optimized additive modeling as a statistically audited extension of fragment-constant schemes, integrating robustness analysis and applicability-domain assessment into a transparent QSPR workflow.