Strandgaard Magnus, Linjordet Trond, Kneiding Hannes, Burnage Arron L, Nova Ainara, Jensen Jan Halborg, Balcells David
Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, Oslo 0315, Norway.
Department of Chemistry, University of Copenhagen, Copenhagen 2100, Denmark.
JACS Au. 2025 Apr 23;5(5):2294-2308. doi: 10.1021/jacsau.5c00242. eCollection 2025 May 26.
Deep generative models yielding transition metal complexes (TMCs) remain scarce despite the key role of these compounds in industrial catalytic processes, anticancer therapies, and the energy transition. Compared to drug discovery within the chemical space of organic molecules, TMCs pose further challenges, including the encoding of chemical bonds of higher complexity and the need to optimize multiple properties. In this work, we developed a generative model for the inverse design of transition metal ligands and complexes, based on the junction tree variational autoencoder (JT-VAE). After implementing a SMILES-based encoding of the metal-ligand bonds, the model was trained with the tmQMg-L ligand library, allowing for the generation of thousands of novel, highly diverse monodentate (κ) and bidentate (κ) ligands, including imines, phosphines, and carbenes. Further, the generated ligands were labeled with two target properties reflecting the stability and electron density of the associated homoleptic iridium TMCs: the HOMO-LUMO gap (ϵ) and the charge of the metal center ( ). This data was used to implement a conditional model that generated ligands from a prompt, with the single- or dual-objective of optimizing either or both the ϵ and properties and allowing for chemical interpretation based on the optimization trajectories. The optimizations also had an impact on other chemical properties, including ligand dissociation energies and oxidative addition barriers. A similar model was implemented to condition ligand generation by solubility and steric bulk.
尽管过渡金属配合物（TMCs）在工业催化过程、抗癌治疗和能源转型中起着关键作用，但能够生成这些化合物的深度生成模型仍然很少。与在有机分子化学空间内进行药物发现相比，TMCs带来了更多挑战，包括对更高复杂性化学键的编码以及优化多种性质的需求。在这项工作中，我们基于连接树变分自编码器（JT-VAE）开发了一种用于过渡金属配体和配合物逆向设计的生成模型。在对金属-配体键实施基于SMILES的编码后，该模型使用tmQMg-L配体库进行训练，从而能够生成数千种新颖、高度多样的单齿（κ）和双齿（κ）配体，包括亚胺、膦和卡宾。此外，生成的配体用反映相关纯配体铱TMCs稳定性和电子密度的两个目标性质进行标记：最高已占分子轨道-最低未占分子轨道能隙（ϵ）和金属中心电荷（ ）。这些数据被用于实现一个条件模型，该模型根据一个提示生成配体，具有优化ϵ和 性质中的一个或两个的单目标或双目标，并允许基于优化轨迹进行化学解释。这些优化对其他化学性质也有影响，包括配体解离能和氧化加成势垒。还实施了一个类似的模型，根据溶解度和空间体积来调节配体生成。
