机器学习用于De Novo分子生成:综合性审查
1School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201219, China.
ACS chemical neuroscience
|February 10, 2026
概括
深度生成模型通过探索广的化学空间来加速分子设计. 这篇评论详细介绍了机器学习模型,挑战和用于下一代药物发现的应用.
科学领域:
- 计算化学和化学信息学
- 人工智能和机器学习
- 药物发现和药物化学
背景情况:
- 深度生成模型为*de novo*分子设计提供了强大的计算工具.
- 它们使得化学空间的有效探索能够超越传统的实验方法.
- 机器学习正在通过产生新型分子结构来彻底改变药物发现.
研究的目的:
- 为机器学习驱动的分子生成提供全面的调查.
- 根据分子表示,模型架构和评估框架系统地组织该领域.
- 分析最先进的生成模型,它们的机制,优势,局限性和故障模式.
主要方法:
- 对生成模型的审查和系统组织:变化自编码器 (VAE),生成对立网络 (GAN),循环神经网络 (RNN),变压器,扩散模型,规范流和混合架构.
- 对算法失效模式和实际部署挑战的分析.
- 讨论分布式学习,目标导向生成和特定治疗领域 (如中枢神经系统药物发现) 的应用.
主要成果:
- 详细的分类学和分析各种深度生成模型架构用于分子设计.
- 批判性评估计算基准和实用药物化学之间的差距,包括合成可行性和实验验证.
- 识别持续存在的挑战和有希望的未来机会,例如基于物理的模型和大型语言模型.
结论:
- 深度生成模型对于*de novo*分子设计至关重要,但在实际部署方面面临挑战.
- 解决合成可行性,实验验证和多参数优化的局限性是下一代药物发现的关键.
- 未来的方向包括整合物理,利用大型语言模型,并利用自主实验室来改进药物设计.
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