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

IF 2.7 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling Pub Date : 2025-03-04 DOI:10.1016/j.jmgm.2025.109004

Aleksandar M. Veselinović , Alla P. Toropova , Andrey A. Toropov , Alessandra Roncaglioni , Emilio Benfenati

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Abstract

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.

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拉斯维加斯算法在药物类化合物固有溶解度预测中的应用

总是能够成功地产生正确输出，但其运行时间取决于随机事件的随机算法被称为拉斯维加斯算法。在本研究中，Las Vegas算法旨在改进由Monte Carlo方法获得的类药物化合物固有溶解度的QSPR模型。利用CORAL软件进行了相应的计算实验。开发的QSPR模型使用一组统计参数进行了严格验证，显示出出色的预测能力和稳健性。结果表明，Las Vegas算法是提高蒙特卡罗技术得到的模型预测潜力的一种合适的方法。此外，该研究确定了影响内在溶解度（增加或减少）的smile符号描述符衍生的关键分子片段。总的来说，这项工作强调了蒙特卡罗方法优化与应用拉斯维加斯算法在构建与构象无关的QSPR模型方面的有效性，该模型具有很强的预测能力，可用于预测类药物化合物的固有溶解度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of molecular graphics & modelling 生物-计算机：跨学科应用

CiteScore

5.50

自引率

6.90%

发文量

216

审稿时长

35 days

期刊介绍： The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.