Enhanced Ligand Discovery through Generative AI and Latent-Space Exploration: Application to the Mizoroki-Heck Reaction

01 March 2024, Version 1


The identification of catalysts that promote chemical reactions is a critical challenge in the production of pharmaceuticals. One of the main bottlenecks in this process is the synthesis of vast libraries of precatalysts, although assessing catalyst effectiveness can be rapidly conducted through high-throughput experimentation. The rational design and development of high-performing precatalysts can circumvent this challenge and lead to important advances. In this study, we apply the transformer-based Kernel-Elastic Autoencoder (KAE) equipped with a conditioned latent space, enabling the targeted generation of ligands with desired steric and electronic properties. Our KAE model has facilitated the identification of a monodentate alkynylphosphine, dubbed MachinePhos A, as an effective precatalyst for forming carbon-carbon bond. Its utility was demonstrated experimentally in the Mizoroki-Heck reaction, using a variety of nitrogen-rich arenes pertinent to pharmaceutical applications.


generative artificial intelligence
molecular design
Palladium catalysis

Supplementary materials

Supporting Information
experimental procedures, characterization, machine learning training protocols, DFT coordinates


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