DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery

arXiv cs.AI / 4/6/2026

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Key Points

The paper introduces DrugPlayGround, a framework intended to objectively benchmark LLMs and embeddings for tasks relevant to drug discovery, such as physiochemical characteristic descriptions, drug synergism, and drug–protein interactions.
It targets evaluation of both chemical/biological reasoning and the ability to predict physiological responses to perturbations caused by drug molecules.
The framework is designed to integrate with domain experts to produce detailed explanations that justify model predictions, not just output correctness.
By addressing the lack of objective assessments comparing LLMs to traditional drug discovery platforms, the work aims to clarify LLM strengths and limitations across multiple stages of the drug pipeline.

Abstract

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.