通过自动化和机器智能实现化学反应的高度并行优化
Joshua W Sin1,2, Siu Lun Chau3, Ryan P Burwood4
1Process Chemistry & Catalysis, Synthetic Molecules Technical Development, F. Hoffmann-La Roche AG, Basel, Switzerland. wing_pong.sin@roche.com.
Nature communications
|July 12, 2025
概括
我们开发了Minerva,这是一个机器学习 (ML) 框架,用于使用自动化高通量实验 (HTE) 优化化学反应. 这种可扩展的工具有效地处理复杂的反应条件,提高了制药合成的产量和选择性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 优化化学反应对于高效的合成至关重要,但高维度和实验噪声等复杂因素带来了重大挑战.
- 自动化高通量实验 (HTE) 加快了数据采集,但需要复杂的方法来有效优化.
- 传统的实验方法可能耗时,可能无法完全探索复杂的反应场景.
研究的目的:
- 开发和验证一个可扩展的机器学习 (ML) 框架,命名为Minerva,用于多目标反应优化.
- 证明框架能够处理现实世界的实验室约束和复杂的化学反应.
- 在学术研究 (催化苏苏基反应) 和工业制药工艺开发中应用Minerva.
主要方法:
- 开发一个可扩展的机器学习 (ML) 框架 (Minerva),将多目标优化与自动化高通量实验 (HTE) 整合起来.
- 与实验数据对比Minerva的性能,重点关注大型并行批次,高维空间和噪音反应数据的效率.
- 通过96井HTE运动对催化苏苏基反应进行实验验证,并用于优化活性药物成分 (API) 合成.
主要成果:
- 米内尔瓦展示了强大的性能,高效地管理大型并行批量,高维的搜索空间和实验噪声.
- 该框架成功地导航了复杂的反应场景,确定了催化苏苏基反应的最佳条件,具有意想不到的反应性.
- 在制药工艺开发中,Minerva优化了两种API合成,在Ni-催化苏苏基和Pd-催化布赫瓦尔德-哈特维格反应中实现了>95%的面积百分比产量和选择性.
结论:
- 米内尔瓦提供了一个可扩展和高效的ML驱动解决方案,用于使用HTE进行多目标反应优化.
- 该框架有效地解决了非贵金属催化和复杂反应优化方面的挑战.
- 在制药工艺开发中Minerva的成功应用凸显了其在改进大规模化工制造方面的潜力.
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