Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.02346 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908934312296448 |
|---|---|
| author | Liu, Tianyu Jiang, Sihan Zhang, Fan Sun, Kunyang Head-Gordon, Teresa Zhao, Hongyu |
| author_facet | Liu, Tianyu Jiang, Sihan Zhang, Fan Sun, Kunyang Head-Gordon, Teresa Zhao, Hongyu |
| contents | Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02346 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery Liu, Tianyu Jiang, Sihan Zhang, Fan Sun, Kunyang Head-Gordon, Teresa Zhao, Hongyu Machine Learning Artificial Intelligence Software Engineering Biomolecules Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages. |
| title | DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery |
| topic | Machine Learning Artificial Intelligence Software Engineering Biomolecules |
| url | https://arxiv.org/abs/2604.02346 |