VNFlow:用于新型分子设计的变化自编码器和规范化流的集成
Jiří Hostaš1, Mohammad S Ghaemi2, Hang Hu2
1Digital Technologies Research Centre, National Research Council Canada, Toronto, ON, Canada. jiri.hostas@nrc-cnrc.gc.ca.
Journal of cheminformatics
|October 25, 2025
概括
生成型人工智能通过创建具有所需性质的新型分子来加速分子发现. 这种新模型有效地优化了药物相似性和合成,克服了化学太空探索的局限性.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 人工智能的人工智能
背景情况:
- 生成型人工智能 (AI) 正在通过探索广的化学空间来彻底改变分子发现.
- 现有的方法,如规范化流量,在平衡复杂的客观优化与采样速度方面面临挑战,特别是在复杂的分子支架上.
- 在当前的生成模型中,生成特定的化合物类别和复杂的结构,如芳香环,仍然是一个障碍.
研究的目的:
- 开发一种能够有效采样新型分子的生成模型.
- 优化关键的分子特性,包括药物相似性,合成可访问性和化学反应性.
- 解决反向分子设计的局限性,特别是对于训练数据有限的场景.
主要方法:
- 采用了与分子生成的变化自编码器集成的规范化流.
- 使用药物相似性的定量估计 (QED) 和合成可访问性 (SA) 评分评估生成的分子.
- 使用密度函数理论 (DFT) 来得的希尔什菲尔德电荷计算了有机酸中原子的电子密度.
- 使用SELFIES (简化分子输入线输入系统) 和组SELFIES直接集成到规范化流.
主要成果:
- 该框架有效地产生了一组多样化的新型有机酸.
- 证明了药物相似性,合成可访问性和电子性质的成功优化.
- 展示了将规范化流与SELFIES/组-SELFIES相结合用于反向分子设计的有效性.
- 当训练数据稀缺时,克服了变化自动编码器的局限性.
结论:
- 开发的生成模型有效地采样新型分子,同时优化复杂的目标.
- 将规范化流与SELFIES/组-SELFIES相结合,解决了反向分子设计的关键局限性,特别是有限的数据.
- 这种方法使化学结构的整体捕获成为可能,为具有优化分子目标的向治疗铺平了道路.
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