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Mining Patents with Large Language Models Elucidates the Chemical Function Landscape
Clayton W Kosonocky1, Claus O Wilke1, Edward M Marcotte1
1The University of Texas at Austin.
Arxiv
|January 10, 2024
Summary
Researchers created a large dataset of chemical functions from patents to discover new molecules. This text-derived approach complements traditional structure-based methods for drug discovery.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Drug Discovery
Background:
- Small molecule discovery aims to create chemicals with specific functions.
- Traditional methods often rely on structure-based approaches.
- Orthogonal methods leveraging chemical literature are being explored.
Conclusions:
- Text-derived functional landscapes can approximate real-world chemical functionality.
- Functional label-guided discovery offers an orthogonal approach to structure-based methods.
- This method aids in designing novel functional molecules.
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