A Linear and Nonlinear QSAR Analysis of Benzimidazole Derivative XY123 in Prostate Cancer Treatment

IF 1.2 4区医学 Q4 CHEMISTRY, MEDICINAL

Letters in Drug Design & Discovery Pub Date : 2024-04-13 DOI:10.2174/0115701808291381240226094729

Bing Li, Xiaoqiang Liu

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引用次数: 0

Abstract

Background: Metastatic Castration-Resistant Prostate Cancer (mCRPC) represents a critical challenge in current prostate cancer treatment. Benzimidazole Derivative XY123 has emerged as a novel inhibitor for its treatment. Objective: This study aims to establish a robust Quantitative Structure-Activity Relationship (QSAR) model for predicting the activity of Benzimidazole Derivative XY123 derivatives, aiding the development of novel anti-prostate cancer drugs. Methods: Utilizing CODESSA software, descriptors were computed based on various moieties of Benzimidazole Derivative XY123 derivatives. Multiple linear regression models were constructed, and both linear and nonlinear QSAR models were developed using heuristics and gene expression programming. Results: The linear model with two descriptors demonstrated the best predictive capacity for inhibitor activity, while the nonlinear model generated through Gene Expression Programming (GEP) exhibited correlation coefficients of 0.83 and 0.82 for the training and test sets, respectively. The average errors were 0.03 and 0.05, indicating the stability and the improved predictive ability of the nonlinear model. Conclusion: The QSAR linear model has an advantage over the nonlinear model in optimizing Benzimidazole Derivative XY123, providing a direction for the development of effective drugs for mCRPC treatment.

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苯并咪唑衍生物 XY123 在前列腺癌治疗中的线性和非线性 QSAR 分析

背景：转移性阉割耐药前列腺癌（mCRPC）是当前前列腺癌治疗中的一大难题。苯并咪唑衍生物 XY123 已成为治疗该病的新型抑制剂。研究目的本研究旨在建立一个稳健的定量结构-活性关系（QSAR）模型，用于预测苯并咪唑衍生物 XY123 的活性，从而帮助开发新型抗前列腺癌药物。方法：利用 CODESSA 软件，根据苯并咪唑衍生物 XY123 衍生物的不同分子计算描述因子。构建了多元线性回归模型，并利用启发式方法和基因表达编程建立了线性和非线性 QSAR 模型。研究结果带有两个描述因子的线性模型对抑制剂活性的预测能力最强，而通过基因表达编程（GEP）生成的非线性模型对训练集和测试集的相关系数分别为 0.83 和 0.82。平均误差分别为 0.03 和 0.05，这表明非线性模型的稳定性和预测能力得到了提高。结论QSAR线性模型在优化苯并咪唑衍生物XY123方面比非线性模型更有优势，为开发治疗mCRPC的有效药物提供了方向。

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

Letters in Drug Design & Discovery 医学-医药化学

CiteScore

1.80

自引率

10.00%

发文量

245

审稿时长

3 months

期刊介绍： Aims & Scope Letters in Drug Design & Discovery publishes letters, mini-reviews, highlights and guest edited thematic issues in all areas of rational drug design and discovery including medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, and structure-activity relationships. The emphasis is on publishing quality papers very rapidly by taking full advantage of latest Internet technology for both submission and review of manuscripts. The online journal is an essential reading to all pharmaceutical scientists involved in research in drug design and discovery.