Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
<|reference_start|>Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications: 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.<|reference_end|>
@article{oniani2024emerging,
    title={Emerging Opportunities of Using Large Language Models for Translation
  Between Drug Molecules and Indications},
    author={David Oniani, Jordan Hilsman, Chengxi Zang, Junmei Wang, Lianjin Cai,
  Jan Zawala, Yanshan Wang},
    journal={arXiv preprint arXiv:2402.09588},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09588},
    primaryClass={cs.AI cs.CL}
}