更快,更多样化的de novo分子优化与双循环增强学习使用增强的SMILES
Esben Jannik Bjerrum1, Christian Margreitter2, Thomas Blaschke2
1Odyssey Therapeutics, Cambridge, MA, USA. esben@odysseytx.com.
Journal of computer-aided molecular design
|June 17, 2023
概括
生成型深度学习和强化学习通过创建新分子来加速药物发现. 一种新的双循环方法提高了分子生成的效率和多样性.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子生成分子生成
背景情况:
- 生成型深度学习和强化学习 (RL) 模型可以设计具有特定属性的新分子.
- 由于计算密集的评分函数,当前的方法面临挑战,这会减缓RL优化过程.
- 高效的分子生成对于加速药物发现和材料科学应用至关重要.
研究的目的:
- 通过生成性深度学习和RL提高分子生成的效率和速度.
- 解决基于RL的分子设计中与得分函数相关的计算瓶.
- 提高产生的分子与已知的配体的多样性和相似性.
主要方法:
- 提出了一种双循环增强学习框架.
- 集成的简化分子线输入系统 (SMILES) 在内部循环中进行增强.
- 重新使用得分计算,并为额外的RL轮引入非正规的SMILES.
主要成果:
- 双循环RL方法显著加快了分子生成和优化过程.
- 通过5-10次增强重复,可以达到最佳性能.
- 该方法导致化合物多样性增加,提升了采样可重复性,并产生了类似于已知的配体的分子.
结论:
- 双循环强化学习与SMILES增强提供了一个更有效和更强大的方法,用于新的分子设计.
- 这种方法有效地减轻了计算成本,并提高了用于药物发现的生成分子的质量.
- 该战略提供了防止模式崩的保护,并增加了化学研究中生成模型的整体实用性.
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