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。

T-Rex: 文本辅助的合成逆向预测

T-Rex: Text-assisted Retrosynthesis Prediction

Deep generative models are able to suggest new organic molecules by
generating strings, trees, and graphs representing their structure. While such
models allow one to generate molecules with desirable properties, they give no
guarantees that the molecules can actually be synthesized in practice. We
propose a new molecule generation model, mirroring a more realistic real-world
process, where (a) reactants are selected, and (b) combined to form more
complex molecules. More specifically, our generative model proposes a bag of
initial reactants (selected from a pool of commercially-available molecules)
and uses a reaction model to predict how they react together to generate new
molecules. We first show that the model can generate diverse, valid and unique
molecules due to the useful inductive biases of modeling reactions.
Furthermore, our model allows chemists to interrogate not only the properties
of the generated molecules but also the feasibility of the synthesis routes. We
conclude by using our model to solve retrosynthesis problems, predicting a set
of reactants that can produce a target product.

本篇论文提出了一种新的分子生成模型，模拟了现实中的制备过程，可以通过选择初试反应物（从商业上可获得的分子池中选择），使用反应模型来预测它们之间的反应生成新的分子，同时可以考虑化合物的性质和合成路径的可行性，通过解决反合成问题，预测出一组可以产生目标产品的反应物。