Current Updates on Recent Developments in Artificial Intelligence in QSAR Modelling for Drug Discovery against Lung Cancer.

IF 3.3 4区医学 Q3 CHEMISTRY, MEDICINAL

Current topics in medicinal chemistry Pub Date : 2025-10-21 DOI:10.2174/0115680266391548250930172321

Deepanshi Chaudhary, Chakresh Kumar Jain

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Abstract

Lung cancer continues to be a leading cause of cancer-related mortality worldwide, underscoring the urgency for innovative and targeted drug discovery strategies. This review critically explores the role of Quantitative Structure-Activity Relationship (QSAR) modelling, particularly its integration with artificial intelligence (AI), in accelerating the identification and optimization of lung cancer therapeutics. Recent progress in multi-target approaches, machine learning (ML) algorithms with mathematical representations, and molecular descriptor engineering has been analyzed, with a special focus on clinical translations. Rather than offering a generic overview, we evaluate how AI-powered QSAR addresses key bottlenecks in drug development, such as data imbalance, model interpretability, and ADMET prediction failures. Notable case studies are examined to highlight translational success stories in lung cancer-specific pathways. This review offers a cohesive synthesis of current advancements, identifies critical gaps and limitations, and proposes future directions for enhancing the real-world applications of QSAR methodologies in oncological drug discovery.

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人工智能在肺癌药物发现QSAR建模中的最新进展。

肺癌仍然是全球癌症相关死亡的主要原因，强调了创新和靶向药物发现策略的紧迫性。这篇综述批判性地探讨了定量构效关系（QSAR）模型的作用，特别是它与人工智能（AI）的结合，在加速肺癌治疗方法的识别和优化中的作用。分析了多靶点方法、具有数学表示的机器学习（ML）算法和分子描述符工程的最新进展，并特别关注临床翻译。我们不是提供一个通用的概述，而是评估人工智能驱动的QSAR如何解决药物开发中的关键瓶颈，如数据不平衡、模型可解释性和ADMET预测失败。值得注意的案例研究进行了检查，以突出在肺癌特异性途径转化成功的故事。这篇综述提供了当前进展的综合，确定了关键的差距和限制，并提出了未来的方向，以加强QSAR方法在肿瘤药物发现中的实际应用。

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

Current topics in medicinal chemistry 医学-医药化学

CiteScore

6.40

自引率

2.90%

发文量

186

审稿时长

3-8 weeks

期刊介绍： Current Topics in Medicinal Chemistry is a forum for the review of areas of keen and topical interest to medicinal chemists and others in the allied disciplines. Each issue is solely devoted to a specific topic, containing six to nine reviews, which provide the reader a comprehensive survey of that area. A Guest Editor who is an expert in the topic under review, will assemble each issue. The scope of Current Topics in Medicinal Chemistry will cover all areas of medicinal chemistry, including current developments in rational drug design, synthetic chemistry, bioorganic chemistry, high-throughput screening, combinatorial chemistry, compound diversity measurements, drug absorption, drug distribution, metabolism, new and emerging drug targets, natural products, pharmacogenomics, and structure-activity relationships. Medicinal chemistry is a rapidly maturing discipline. The study of how structure and function are related is absolutely essential to understanding the molecular basis of life. Current Topics in Medicinal Chemistry aims to contribute to the growth of scientific knowledge and insight, and facilitate the discovery and development of new therapeutic agents to treat debilitating human disorders. The journal is essential for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important advances.