最近的进展从计算机辅助药物设计到人工智能药物设计
Keran Wang1,2, Yanwen Huang3, Yan Wang4
1State Key Laboratory of Natural Medicines and, Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University Nanjing 210009 China youqd@163.com leiwang.91@cpu.edu.cn +86 025 83271351 +86 15261483858.
RSC medicinal chemistry
|November 4, 2024
概括
人工智能药物设计 (AIDD) 通过增强计算机辅助药物设计 (CADD) 加快药物发现. AIDD探索新的化学空间,以更快地识别和生成潜在的候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 人工智能在药物发现中的作用
背景情况:
- 计算机辅助药物设计 (CADD) 使用基于结构和基于连接体的方法预测分子活性和向相互作用.
- 人工智能药物设计 (AIDD) 代表了CADD的先进代,利用增加的数据和优化的模型.
- 在过去的十年中,AIDD显著推进了药物发现,提供了新的可能性.
研究的目的:
- 概述CADD的核心工作流程和组件.
- 通过示范性的AIDD案例来说明AI在药物发现中的不断变化的作用.
- 为了比较CADD和AIDD的独特特征和功能.
主要方法:
- 对CADD原则的审查,包括基于结构和基于连接体的方法.
- 展示近期的AI驱动的药物发现实例.
- 对AIDD在化学图书馆选,链接生成和de novo分子生成方面的影响进行分析.
主要成果:
- 艾迪加速药物发现过程,包括蛋白质折叠,性质预测和分子生成.
- AIDD促进了关键阶段,如目标识别,高通量选 (HTS) 和合成路线预测.
- 通过AIDD,可以超越当前的限制,探索新的化学空间.
结论:
- AIDD显著增强了传统的CADD,提供了加快的时间表和更广泛的化学可能性的探索.
- 人工智能的整合改变了药物发现,从初始查到生成全新的分子实体.
- AIDD提出了一个强大的范式转变,推动了药物设计和发现的界限.
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