在生成分子设计中直接优化合成能力,使用回复合成模型.
Jeff Guo1,2, Philippe Schwaller1,2
1École Polytechnique Fédérale de Lausanne (EPFL) Switzerland jeff.guo@epfl.ch philippe.schwaller@epfl.ch.
Chemical science
|March 24, 2025
概括
产生性分子设计现在可以通过回复合成模型直接优化合成能力. 这种方法有效地为药物发现和材料科学创造了新型分子,克服了传统启发式的局限性.
科学领域:
- 计算化学是一种计算化学.
- 化学领域的人工智能
- 分子设计分子设计.
背景情况:
- 合成性是生成分子设计的一个关键挑战.
- 目前的方法依赖于启发式或后期逆转合成分析.
- 由于计算成本,将可合成性预测直接集成到生成中是很困难的.
研究的目的:
- 在生成模型中开发一种直接优化分子合成能力的方法.
- 探索逆合成模型在目标定向分子生成中的使用.
- 为了比较直接回复合成整合与基于启发式方法的有效性.
主要方法:
- 采用了能够直接优化的样本效率生成模型.
- 将集成的回复合成模型集成到目标定向的生成循环中.
- 在有限的计算预算下评估药物发现任务的性能.
主要成果:
- 成功生成了符合多参数优化标准的可合成分子.
- 发现常见的可合成性启发学与生物活性分子的逆合成可溶性相关.
- 证明了功能材料启发式的相关性减少,突出显示了直接回复合成集成的优势.
- 展示了如何过度依赖启发式分析可以忽视有前途的分子.
结论:
- 使用回复合成模型直接优化合成能力是可以通过高效的生成方法实现的.
- 复杂合成模型对各种分子类,特别是功能材料的启发学具有优势.
- 这种方法增强了用于各种应用的新型,可合成的分子的发现.
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