深度生成模型在De Novo中产生药物分子
Chao Pang1,2, Jianbo Qiao1,2, Xiangxiang Zeng3
1School of Software, Shandong University, Jinan 250100, China.
Journal of chemical information and modeling
|November 7, 2023
概括
人工智能,特别是深度生成模型,通过高效生成具有所需性质的分子来加速新药发现. 这篇评论强调了人工智能.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 传统的药物发现是劳动密集型和昂贵的,依赖于实验性分子优化.
- 人工智能 (AI) 显示出对高效和快速的药物样分子产生显著的希望.
- 深度生成模型是人工智能驱动的*de novo*分子设计的最前沿.
研究的目的:
- 通过使用深度生成模型,审查分子生成的最新进展.
- 为分子生成提供先进的人工智能框架的全面比较.
- 识别挑战,并建议未来的研究方向在基于AI的药物发现.
主要方法:
- 关于分子生成深度生成模型的文献综述.
- 专注于分子表示,公共数据库和数据处理工具.
- 对领先的基于人工智能的分子生成框架和设计策略进行比较分析.
主要成果:
- 深度生成模型在*de novo*生成具有特定属性的药物样分子方面表现出色.
- 介绍了当前人工智能模型和分子设计策略的全面比较.
- 关键的研究缺陷包括数据库的局限性,缺乏3D信息和不准确的评估指标.
结论:
- 人工智能,特别是深度生成模型,为新药发现提供了强大而高效的方法.
- 解决目前数据,表示和评估方面的局限性对于推进分子设计中的AI至关重要.
- 未来的方向包括改进数据库,整合3D结构信息,并开发精确的AI驱动药物发现指标.
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