步行者指南 深度学习驱动的生成化学
Yan Ivanenkov1, Bogdan Zagribelnyy2, Alex Malyshev1
1Insilico Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong.
ACS medicinal chemistry letters
|July 19, 2023
概括
本文审查了人工智能 (AI) 产生的分子结构,重点是合成和实验验证. 它评估了这些AI驱动药物设计 (AIDD) 分子对药物化学家的相关性和新性.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 人工智能 (AI) 越来越多地被用于药物设计.
- 评估人工智能生成的分子需要医学化学专业知识.
- 实验验证对于评估AI驱动药物设计 (AIDD) 结果至关重要.
研究的目的:
- 审查最近人工智能生成的分子结构.
- 分析这些结构的合成和体外验证.
- 评估AI产生的分子在药物化学中的新性和相关性.
主要方法:
- 关于人工智能生成分子的最新研究的文献综述.
- 实验验证数据的分析 (合成,体外试验).
- 从药物化学家的角度评估分子相关性和新性.
主要成果:
- 确定了人工智能生成的分子结构的关键趋势.
- 评估了人工智能设计化合物的实验验证成功率.
- 强调将人工智能与传统药物化学实践相结合的重要性.
结论:
- 人工智能生成的分子显示出希望,但需要严格的实验验证.
- 药物化学家在合格的AIDD研究中发挥着至关重要的作用.
- 这一审查为评估AIDD成果提供了一个框架.
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