动态语言模型表示用于多目标反应优化

中文摘要 AI 辅助

在化学合成中，优化涉及产率、选择性和安全性等多目标的化学反应至关重要，而模型驱动的方法关键依赖于反应组分的表示方式。已有的特征化方法要么如独热编码那样缺乏化学信息，要么如分子描述符那样难以在不同化学组分间通用。对于结构和功能多样的组分，共享表示应包含什么内容尚不清楚。构建这样的表示本身就是一个具有挑战性的研究任务，且必须针对每个新的反应系统重新进行。在此，我们通过从文本中动态学习反应表示来绕过这一步骤。反应条件的文本描述由微调的语言模型编码，该模型与高斯过程代理模型联合训练，在多目标贝叶斯优化循环中产生任务自适应的表示。在镍和钯催化的交叉偶联反应中，无论是顺序还是并行实验模式，该方法相比描述符库或独热编码，以更少的实验次数达到优化收敛。前瞻性地应用于涉及混合配体齿合度和非均相添加剂的钯催化氰化反应，以及跨越手性铱和钌催化剂家族的三目标不对称氢化反应，两轮高通量实验（192个反应，每个设计空间不到3%）所得条件直接放大至克级，分离产率分别为94%和84%，后者对映体过量达99.6%。

英文摘要

Optimising chemical reactions across multiple objectives, such as yield, selectivity, and safety, is central to chemical synthesis, and model-driven approaches depend critically on how reaction components are represented. Established featurisations are either chemically uninformative, as with one-hot encodings, or, as with molecular descriptors, do not readily extend across chemically distinct components. For structurally and functionally diverse components, it is therefore unclear what a shared representation should contain. Constructing such a representation is itself a challenging research undertaking that must be revisited for each new reaction system. Here we bypass this step by learning the reaction representation dynamically from text. Textual descriptions of reaction conditions are encoded by a fine-tuned language model trained jointly with Gaussian process surrogates, yielding task-adaptive representations within a multi-objective Bayesian optimisation loop. Across nickel- and palladium-catalysed cross-couplings in both sequential and parallel experimentation regimes, this approach reaches optimisation convergence in fewer experiments than descriptor libraries or one-hot encoding. Applied prospectively to a palladium-catalysed cyanation spanning mixed ligand denticity and heterogeneous additives, and to a three-objective asymmetric hydrogenation across chiral iridium and ruthenium catalyst families, two rounds of high-throughput experimentation (192 reactions, under 3% of each design space) delivered conditions translating directly to gram scale in 94% and 84% isolated yield, the latter at 99.6% enantiomeric excess.

发表机构

• F. Hoffmann-La Roche AG（罗氏制药公司）
• EPFL（洛桑联邦理工学院）
• Nanyang Technological University（南洋理工大学）

机构由 AI 辅助整理，请以论文原文为准。