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

We introduce T-Rex, an interactive object counting model designed to first
detect and then count any objects. We formulate object counting as an open-set
object detection task with the integration of visual prompts. Users can specify
the objects of interest by marking points or boxes on a reference image, and
T-Rex then detects all objects with a similar pattern. Guided by the visual
feedback from T-Rex, users can also interactively refine the counting results
by prompting on missing or falsely-detected objects. T-Rex has achieved
state-of-the-art performance on several class-agnostic counting benchmarks. To
further exploit its potential, we established a new counting benchmark
encompassing diverse scenarios and challenges. Both quantitative and
qualitative results show that T-Rex possesses exceptional zero-shot counting
capabilities. We also present various practical application scenarios for
T-Rex, illustrating its potential in the realm of visual prompting.

T-Rex 是一个交互式的物体计数模型，通过视觉提示将物体计数形式化为开放集物体检测任务，可通过标记参考图像上的点或框来指定感兴趣的物体，然后检测具有相似模式的所有物体，并通过 T-Rex 的视觉反馈进行交互式地修正计数结果。该模型在全面的场景和挑战下展现出优异的零样例物体计数能力，且在视觉提示领域具有潜在的实际应用场景。