ArticleDigital discovery2023
Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions.
Article in Digital discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions.ACS omega · 2026Article
- Generative AI-Driven Discovery of Next-Generation Electrolytes for Alkali Metal Batteries.Journal of chemical information and modeling · 2026Article
- Explainable artificial intelligence for materials discovery: application to catalysts for the HER and ORR.Chemical science · 2026Article
- A novel electrode design using Cu-TCPP MOF-modified MWCNT for efficient electro-organic cross coupling synthesis of biphenyl derivatives in a Urea/Chol-Cl DES system as a green and sustainable electrolyte.Scientific reports · 2025Article
- Reaction-conditioned generative model for catalyst design and optimization with CatDRX.Communications chemistry · 2025Article
- From Spectra to Structure: AI-PoweredAnalytical chemistry · 2025Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes.JACS Au · 2025Article
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Article
- Generative Artificial Intelligence for Designing Multi-Scale Hydrogen Fuel Cell Catalyst Layer Nanostructures.ACS nano · 2024Article
- A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target.Chemical science · 2024Article
- Unveiling human origins of replication using deep learning: accurate prediction and comprehensive analysis.Briefings in bioinformatics · 2023Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The need for more efficient catalytic processes is ever-growing, and so are the costs associated with experimentally searching chemical space to find new promising catalysts. Despite the consolidated use of density functional theory (DFT) and other atomistic models for virtually screening molecules based on their simulated performance, data-driven approaches are rising as indispensable tools for designing and improving catalytic processes. Here, we present a deep learning model capable of generating new catalyst-ligand candidates by self-learning meaningful structural features solely from their language representation and computed binding energies. We train a recurrent neural network-based Variational Autoencoder (VAE) to compress the molecular representation of the catalyst into a lower dimensional latent space, in which a feed-forward neural network predicts the corresponding binding energy to be used as the optimization function. The outcome of the optimization in the latent space is then reconstructed back into the original molecular representation. These trained models achieve state-of-the-art predictive performances in catalysts' binding energy prediction and catalysts' design, with a mean absolute error of 2.42 kcal mol
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