  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.
