生成模型作为化学科学的新兴范式
Dylan M Anstine1, Olexandr Isayev1
1Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Journal of the American Chemical Society
|April 13, 2023
概括
生成模型为化学逆向设计提供了强大的机器学习方法, 本综述分析了关键的生成算法,强调了它们在化学应用中的潜力和挑战.
科学领域:
- 计算化学
- 机器学习
- 材料科学
背景情况:
- 设计化学物种的传统计算方法涉及评估众多候选物种,这一过程通常受到计算成本的限制.
- 从结构中学习属性分布的区分模型面临着有效探索所需属性的化学空间的挑战.
- 反向设计旨在直接从目标特性中生成化学结构,提供更有效的替代方案.
研究的目的:
- 为化学逆向设计提供生成模型技术的概述和批判性分析.
- 探索机器学习的应用,特别是生成模型,以发现具有特定特性的新化学化合物.
- 比较不同的生成算法,并讨论它们对化学应用的适用性.
主要方法:
- 综述和分析流行的生成算法,包括生成对抗网络 (GAN),变化自编码器 (VAE),流模型和扩散模型.
- 探索生成模型的数学基础,专注于它们学习联合概率分布的能力.
- 在化学结构生成的背景下,对每个模型类型的优缺点进行批判性评估.
主要成果:
- 生成模型通过直接生成具有所需性质的分子,从歧视性方法提供一个范式的转变.
- 突出了GAN,VAE,流量和扩散模型之间的关键差异,说明了它们生成化学物种的独特机制.
- 讨论了将这些生成模型应用于现实化学问题的近期成功和持续挑战.
结论:
- 生成模型为加速发现具有定制功能的新化学化合物提供了一个有前途的途径.
- 需要进一步的研究来克服当前的挑战,并充分实现化学设计中的生成人工智能的潜力.
- 审查的算法为开发下一代化学和材料科学计算工具提供了基础.
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