OM-Diff:利用引导等变量去噪扩散反向设计有机金属催化剂

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
François Cornet, Bardi Benediktsson, Bjarke Hastrup, Mikkel N. Schmidt and Arghya Bhowmik
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引用次数: 0

摘要

有机金属配合物在均相催化和其他技术应用中无处不在。由于可能的金属配体组合和配体与配体之间的相互作用种类繁多,因此针对特定应用优化此类配合物极具挑战性。在此,我们提出了基于扩散生成模型的反向设计框架 OM-Diff,用于从头开始对此类复合物进行室内设计。鉴于催化剂空间结构的重要性,该模型直接在 3$D 空间的全原子(包括氢)表征上运行。为了处理该数据表示固有的对称性,OM-Diff 结合了等变扩散模型和等变性质预测器,以便在推理时驱动采样。该模型可以有条件地生成训练数据集之外的新型配体。我们通过设计一系列交叉耦合反应的催化剂,并通过 DFT 计算验证所提出的新化合物,证明了所提出方法的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion†

OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion†

Organometallic complexes are ubiquitous in numerous technological applications, and in particular in homogeneous catalysis. Optimization of such complexes for specific applications is challenging due to the large variety of possible metal–ligand combinations and ligand–ligand interactions. Here we present OM-Diff, an inverse-design framework based on a diffusion generative model for in silico design of such complexes. Due to the importance of the spatial structure of a catalyst, the model operates on all-atom (including H) representations in 3D space. To handle the symmetries inherent to that data representation, OM-Diff combines an equivariant diffusion model with an equivariant property predictor. The diffusion model generates ligands conditioned on a specified metal-center, while the property predictor guides the generation towards novel complexes with desired properties. We demonstrate the potential of OM-Diff by designing optimized catalysts for a family of cross-coupling reactions, and validating a selection of novel proposed compounds with DFT calculations.

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CiteScore
2.80
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