A drug molecule is a substance that changes the organism's mental or physical
state. Every approved drug has an indication, which refers to the therapeutic
use of that drug for treating a particular medical condition. While the Large
Language Model (LLM), a generative Artificial Intelligence (AI) technique, has
recently demonstrated effectiveness in translating between molecules and their
textual descriptions, there remains a gap in research regarding their
application in facilitating the translation between drug molecules and
indications, or vice versa, which could greatly benefit the drug discovery
process. The capability of generating a drug from a given indication would
allow for the discovery of drugs targeting specific diseases or targets and
ultimately provide patients with better treatments. In this paper, we first
propose a new task, which is the translation between drug molecules and
corresponding indications, and then test existing LLMs on this new task.
Specifically, we consider nine variations of the T5 LLM and evaluate them on
two public datasets obtained from ChEMBL and DrugBank. Our experiments show the
early results of using LLMs for this task and provide a perspective on the
state-of-the-art. We also emphasize the current limitations and discuss future
work that has the potential to improve the performance on this task. The
creation of molecules from indications, or vice versa, will allow for more
efficient targeting of diseases and significantly reduce the cost of drug
discovery, with the potential to revolutionize the field of drug discovery in
the era of generative AI.

药物分子和适应症之间的翻译是一个新颖的任务，本文评估了九种 T5 大型语言模型在此任务上的表现，并讨论了未来的改进方向。

利用大型语言模型在药物分子和适应症之间进行翻译的新机遇

Emerging Opportunities of Using Large Language Language Models for  Translation Between Drug Molecules and Indications

When pneumonia is not found on a chest X-ray, should the report describe this
negative observation or omit it? We argue that this question cannot be answered
from the X-ray alone and requires a pragmatic perspective, which captures the
communicative goal that radiology reports serve between radiologists and
patients. However, the standard image-to-text formulation for radiology report
generation fails to incorporate such pragmatic intents. Following this
pragmatic perspective, we demonstrate that the indication, which describes why
a patient comes for an X-ray, drives the mentions of negative observations and
introduce indications as additional input to report generation. With respect to
the output, we develop a framework to identify uninferable information from the
image as a source of model hallucinations, and limit them by cleaning
groundtruth reports. Finally, we use indications and cleaned groundtruth
reports to develop pragmatic models, and show that they outperform existing
methods not only in new pragmatics-inspired metrics (+4.3 Negative F1) but also
in standard metrics (+6.3 Positive F1 and +11.0 BLEU-2).

当胸部 X 光片上未发现肺炎时，报告是否应该描述这个否定观察结果还是省略？我们提出这个问题不能仅仅从 X 光片来回答，而是需要从沟通角度出发，从放射科医生与患者之间的沟通目的来解答。为了考虑这个沟通角度，我们演示了指示因素对否定观察的影响，并将指示因素作为附加输入用于报告生成。我们提出了一个框架来识别作为模型幻觉来源的不可推测信息，并通过清理真实报告来限制幻觉。最后，我们使用指示因素和清理后的真实报告来开发了实用的模型，并展示它们在新的实用度度量标准（+4.3 负面 F1）和常规度量标准（+6.3 正面 F1 和 + 11.0 BLEU-2）中均优于现有方法。