Bibliographic Details
| Title: |
T-Rex: Text-assisted Retrosynthesis Prediction |
| Authors: |
Liu, Yifeng, Xu, Hanwen, Fang, Tangqi, Xi, Haocheng, Liu, Zixuan, Zhang, Sheng, Poon, Hoifung, Wang, Sheng |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2401.14637 |
| Accession Number: |
edsarx.2401.14637 |
| Database: |
arXiv |
