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Thanawat Thaingtamtanha1, Rahul Ravichandran1, Francesco Gentile1,2
1Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, Canada.
Expert opinion on drug discovery
|May 19, 2025
概括
人工智能 (AI) 正通过增强虚拟查 (VS) 来彻底改变药物发现. 人工智能改进了基于连接体 (LBVS) 和基于结构 (SBVS) 的方法,加速了潜在药物候选者的识别.
科学领域:
- 药物发现 药物发现 药物发现
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 人工智能 (AI) 是现代药物发现的关键技术.
- 虚拟查 (VS) 是一个关键的早期过程,用于识别潜在的候选药物.
- 人工智能显著增强了基于连接体的虚拟查 (LBVS) 和基于结构的虚拟查 (SBVS).
研究的目的:
- 在药物发现中提供AI应用的概述,重点关注LBVS和SBVS.
- 突出AI导致新生物活性分子的识别和验证的潜在案例.
- 讨论AI与定量结构-活动关系 (QSAR) 建模和先进的SBVS技术的整合.
主要方法:
- 在2025年3月之前的研究中进行文献搜索.
- 在LBVS中审查人工智能应用,包括定量结构-活动关系 (QSAR) 建模.
- 分析AI在增强基于结构的虚拟选 (SBVS) 技术中的作用,例如分子对接和动态模拟.
主要成果:
- 人工智能正在通过利用大型数据集和提高可扩展性来改变VS.
- 人工智能驱动的LBVS和SBVS方法已经成功识别和验证了新的生物活性分子.
- 人工智能提高了候选药物识别的效率和精度.
结论:
- 人工智能显示出极大的潜力,可以简化和改进药物发现过程.
- 人工智能的持续进步对于克服数据策划和模型验证方面的挑战至关重要.
- 人工智能与实验方法的有效整合是实现其对药物发现的全部影响的关键.
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