# Reaction-conditioned generative model for catalyst design and optimization with CatDRX

## TL;DR

CatDRX is a new AI framework that generates and predicts catalysts for chemical reactions by considering reaction conditions and chemical knowledge.

## Contribution

Introduces CatDRX, a reaction-conditioned generative model for catalyst design that integrates optimization and validation using chemical knowledge.

## Key findings

- CatDRX achieves competitive performance in predicting catalytic yield and activity.
- The model enables effective catalyst generation tailored to specific reaction conditions.
- Case studies demonstrate the framework's utility in catalyst discovery across reaction space.

## Abstract

Designing effective catalysts is a key process for optimizing catalytic reactions to reduce time and waste during scale-up. Recently proposed approaches, including generative models, show promise in identifying new catalysts. However, they are mostly developed for specific reaction classes and predefined fragment categories without considering reaction components, limiting the exploration of novel catalysts across reaction space. Here, we present CatDRX, a catalyst discovery framework powered by a reaction-conditioned variational autoencoder generative model for generating catalysts and predicting their catalytic performance. The model is pre-trained on a broad reaction database and fine-tuned for downstream reactions. Our approach achieves competitive performance in both yield and related catalytic activity prediction. Additionally, it enables effective generation of potential catalysts given reaction conditions by integrating optimization toward desired properties and validation based on reaction mechanisms and chemical knowledge, as demonstrated in various case studies. This work helps facilitate and advance catalyst design and discovery for chemical and pharmaceutical industries.

Designing effective catalysts is a key process for optimizing catalytic reactions, however, existing generative approaches are often limited to specific reaction classes and predefined fragment categories. Here, the authors present CatDRX, a catalyst discovery framework powered by a reaction-conditioned variational autoencoder to generate potential catalysts and predict their activities, integrating optimization and validation based on reaction mechanisms and chemical knowledge.