Revolutionizing Drug Discovery: A Comprehensive Review of Computer-Aided Drug Design Approaches

Bharti Oli
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Abstract

Abstract: Computer-Aided Drug Design (CADD) has significantly advanced the drug discovery process, offering tools to enhance efficiency and reduce costs. This review explores essential CADD methodologies, including molecular docking, virtual screening, ADMET profiling, homology modeling, and Quantitative Structure-Activity Relationship (QSAR) models. Molecular docking predicts interactions between drugs and targets, while virtual screening evaluates large compound libraries to identify promising candidates. ADMET profiling assesses pharmacokinetic and toxicological properties early in development. Homology modeling constructs three-dimensional protein models to aid target identification, and QSAR models predict biological activities based on chemical structures. These integrated approaches streamline drug development, providing a robust framework for modern pharmaceutical research.
革新药物发现:计算机辅助药物设计方法综述
摘要:计算机辅助药物设计(CADD)大大推进了药物发现过程,提供了提高效率和降低成本的工具。本综述探讨了基本的 CADD 方法,包括分子对接、虚拟筛选、ADMET 分析、同源建模和定量结构-活性关系 (QSAR) 模型。分子对接预测药物与靶点之间的相互作用,而虚拟筛选则评估大型化合物库,以确定有希望的候选药物。ADMET 分析可在研发早期评估药代动力学和毒理学特性。同源建模可构建三维蛋白质模型来帮助识别靶点,QSAR 模型则可根据化学结构预测生物活性。这些综合方法简化了药物开发过程,为现代药物研究提供了一个强大的框架。
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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