BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction

TL;DR

BatGPT-Chem is a large language model designed for retrosynthesis prediction, integrating extensive chemical data and advanced training techniques to improve accuracy, generalization, and zero-shot capabilities in synthetic chemistry analysis.

Contribution

This paper introduces BatGPT-Chem, a 15-billion-parameter model that unifies chemical tasks using natural language and SMILES, advancing AI-driven retrosynthesis prediction.

Findings

Outperforms existing AI methods in retrosynthesis tasks

Demonstrates strong zero-shot prediction capabilities

Achieves significant improvements on benchmark tests

Abstract

Retrosynthesis analysis is pivotal yet challenging in drug discovery and organic chemistry. Despite the proliferation of computational tools over the past decade, AI-based systems often fall short in generalizing across diverse reaction types and exploring alternative synthetic pathways. This paper presents BatGPT-Chem, a large language model with 15 billion parameters, tailored for enhanced retrosynthesis prediction. Integrating chemical tasks via a unified framework of natural language and SMILES notation, this approach synthesizes extensive instructional data from an expansive chemical database. Employing both autoregressive and bidirectional training techniques across over one hundred million instances, BatGPT-Chem captures a broad spectrum of chemical knowledge, enabling precise prediction of reaction conditions and exhibiting strong zero-shot capabilities. Superior to existing AI methods, our model demonstrates significant advancements in generating effective strategies for complex molecules, as validated by stringent benchmark tests. BatGPT-Chem not only boosts the efficiency and creativity of retrosynthetic analysis but also establishes a new standard for computational tools in synthetic design. This development empowers chemists to adeptly address the synthesis of novel compounds, potentially expediting the innovation cycle in drug manufacturing and materials science. We release our trial platform at \url{https://www.batgpt.net/dapp/chem}.