Las Vegas algorithm in the prediction of intrinsic solubility of drug-like compounds

• Published in: Journal of molecular graphics & modelling Vol. 137; p. 109004
• Main Authors: Veselinović, Aleksandar M., Toropova, Alla P., Toropov, Andrey A., Roncaglioni, Alessandra, Benfenati, Emilio
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2025
• Subjects: Algorithms; CORAL software; Intrinsic solubility; Las Vegas algorithm; Monte Carlo Method; Pharmaceutical Preparations - chemistry; QSAR; Quantitative Structure-Activity Relationship; SMILES; Software; Solubility; Las Vegas algorithm; SMILES; QSAR; CORAL software; Intrinsic solubility
• ISSN: 1093-3263; 1873-4243; 1873-4243
• DOI: 10.1016/j.jmgm.2025.109004

A randomized algorithm that always succeeds in producing a correct output, but whose running time depends on random events is known as a Las Vegas algorithm. In this study, the Las Vegas algorithm aimed to improve QSPR models of intrinsic solubility of drug-like compounds obtained by the Monte Carlo method. Corresponding computational experiments were carried out with the CORAL software. The developed QSPR models were rigorously validated using a battery of statistical parameters, demonstrating excellent predictive ability and robustness. It has been shown, that the Las Vegas algorithm is a suitable way to improve the predictive potential of models obtained with the Monte Carlo technique. Additionally, the study identified key molecular fragments derived from the SMILES notation descriptors that influence the intrinsic solubility (increase or decrease). Overall, this work underscores the efficacy of the Monte Carlo method optimization with applied Las Vegas algorithm in constructing conformation-independent QSPR models with strong predictive power for prediction of intrinsic solubility of drug-like compounds. [Display omitted] •QSPR models for intrinsic aqueous solubility were developed.•Monte Carlo optimization method was employed to construct QSPR models.•Las Vegas algorithm was implemented within Monte Carlo optimization method.•Different methods were applied for QSPR models predictability determination.•Molecular fragments with influence on studied property were determined.