ChemBART: A Pre-trained BART Model Assisting Organic Chemistry Analysis

TL;DR

ChemBART is a pre-trained language model based on BART that unifies multiple chemical analysis tasks, including synthesis planning and property prediction, demonstrating improved efficiency and accuracy validated through wet-lab experiments.

Contribution

Introduces ChemBART, a multi-task, reaction-focused pre-trained model that advances chemical synthesis planning and analysis beyond single-task approaches.

Findings

Achieves ~30% yield improvement over benchmarks.

Enables multi-step synthesis route design.

Validates predictions through wet-lab experiments.

Abstract

Recent advances in large language models (LLMs) have demonstrated transformative potential across diverse fields. While LLMs have been applied to molecular simplified molecular input line entry system (SMILES) in computer-aided synthesis planning (CASP), existing methodologies typically address single tasks, such as precursor prediction. We introduce ChemBART, a SMILES-based LLM pre-trained on chemical reactions, which enables a unified model for multiple downstream chemical tasks--achieving the paradigm of "one model, one pre-training, multiple tasks." By leveraging outputs from a mask-filling pre-training task on reaction expressions, ChemBART effectively solves a variety of chemical problems, including precursor/reagent generation, temperature-yield regression, molecular property classification, and optimizing the policy and value functions within a reinforcement learning framework, integrated with Monte Carlo tree search for multi-step synthesis route design. Unlike single-molecule pre-trained LLMs constrained to specific applications, ChemBART addresses broader chemical challenges and integrates them for comprehensive synthesis planning. Crucially, ChemBART-designed multi-step synthesis routes and reaction conditions directly inspired wet-lab validation, which confirmed shorter pathways with ~30% yield improvement over literature benchmarks. Our work validates the power of reaction-focused pre-training and showcases the broad utility of ChemBART in advancing the complete synthesis planning cycle.