T-Rex: Text-assisted Retrosynthesis Prediction
Yifeng Liu Hanwen Xu Tangqi Fang Haocheng Xi Zixuan Liu Sheng Zhang Hoifung Poon Sheng Wang
As a fundamental task in computational chemistry, retrosynthesis prediction
aims to identify a set of reactants to synthesize a target molecule. Existing
template-free approaches only consider the graph structures of the target
molecule, which often cannot generalize well to rare reaction types and large
molecules. Here, we propose T-Rex, a text-assisted retrosynthesis prediction
approach that exploits pre-trained text language models, such as ChatGPT, to
assist the generation of reactants. T-Rex first exploits ChatGPT to generate a
description for the target molecule and rank candidate reaction centers based
both the description and the molecular graph. It then re-ranks these candidates
by querying the descriptions for each reactants and examines which group of
reactants can best synthesize the target molecule. We observed that T-Rex
substantially outperformed graph-based state-of-the-art approaches on two
datasets, indicating the effectiveness of considering text information. We
further found that T-Rex outperformed the variant that only use ChatGPT-based
description without the re-ranking step, demonstrate how our framework
outperformed a straightforward integration of ChatGPT and graph information.
Collectively, we show that text generated by pre-trained language models can
substantially improve retrosynthesis prediction, opening up new avenues for
exploiting ChatGPT to advance computational chemistry. And the codes can be
found at https://github.com/lauyikfung/T-Rex.