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 this https URL

利用预训练文本语言模型（如 ChatGPT），我们提出了一个以文本辅助的逆合成预测方法 T-Rex，它通过生成目标分子的描述和分子图的结构来生成候选反应中心，并使用候选反应物的描述进行重新排序，从而更好地合成目标分子。通过对两个数据集的实验，我们观察到 T-Rex 明显优于基于图结构的最先进方法，在不仅使用重新排序步骤的 ChatGPT 描述的变体中 T-Rex 也表现出色，这表明我们的框架优于 ChatGPT 和图信息的简单集成方法。综上所述，我们展示了预训练语言模型生成的文本可以大大提高逆合成预测，为利用 ChatGPT 推进计算化学开辟了新的途径。