Summary of Emerging Opportunities Of Using Large Language Models For Translation Between Drug Molecules and Indications, by David Oniani et al.
Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
by David Oniani, Jordan Hilsman, Chengxi Zang, Junmei Wang, Lianjin Cai, Jan Zawala, Yanshan Wang
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In a breakthrough in artificial intelligence-assisted drug discovery, researchers propose a new task: translating between drug molecules and their corresponding indications. This capability has the potential to accelerate the development of targeted treatments for specific diseases. The study evaluates various Large Language Model (LLM) architectures on two public datasets, demonstrating early success in this challenging task. While current results are promising, there is still room for improvement. By harnessing the power of generative AI, scientists can efficiently discover new drugs and reduce costs, revolutionizing the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have found a way to use artificial intelligence (AI) to help discover new medicines! They’re working on translating between medicine molecules and what they treat, like headaches or cancer. This could speed up finding cures for specific diseases. The AI models are still learning, but it’s an exciting step forward. |
Keywords
» Artificial intelligence » Large language model
