用于分子设计的生成人工智能:进步和挑战
Yan Sun1,2, Lianghong Chen2, Zihao Jing2
1Department of Biochemistry, University of Western Ontario, London, Ontario N6A 5C1, Canada.
Journal of chemical information and modeling
|November 18, 2025
概括
生成型人工智能 (AI) 能够使药物发现和生物材料的新分子设计成为可能. 本文重点介绍了人工智能架构,应用以及创造具有理想性质的新型分子的未来挑战.
科学领域:
- 化学,计算机科学,生物信息学
背景情况:
- 传统的分子设计方法昂贵且低效.
- 预测机器学习模型在精准医学上存在局限性.
- 生成性人工智能为新的分子创造提供了一个范式的转变.
研究的目的:
- 调查分子设计的生成AI快速发展的领域.
- 引入生成架构和优化策略.
- 检查人工智能驱动分子设计中的应用和挑战.
主要方法:
- 对生成性AI架构 (VAE,GAN,规范化流,扩散模型) 的审查.
- 优化策略的分析 (采样,培训,后代).
- 检查小型和大型分子设计中的应用.
主要成果:
- 生成型人工智能在设计具有复杂要求的分子方面展示了前所未有的能力.
- 人工智能驱动的新型抗生素在体内有效性的发现展示了翻译潜力.
- 确定了生成分子设计的关键挑战和未来方向.
结论:
- 生成型人工智能正在彻底改变药物发现和生物材料的分子设计.
- 应对数据稀缺和多式联通等挑战对于未来的进步至关重要.
- 使用不确定性意识策略优化多个目标将增强分子设计系统.
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