AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agents

Researchers have introduced AssayBench, a novel benchmarking framework designed to evaluate the performance of large language models (LLMs) and agents in simulating cellular behavior at the assay level. This development is crucial for realizing the concept of a virtual cell, a computational model that can predict the effects of cellular perturbations and accelerate biological discovery. AssayBench provides a standardized platform for assessing the capabilities of LLMs and agents in performing in silico phenotypic screens, which can potentially replace or complement traditional wet-lab experiments¹. By leveraging large-scale biological data collections and advances in machine learning, AssayBench enables the evaluation of virtual cell models under various scenarios, facilitating the identification of the most effective models. This matters to practitioners because a reliable virtual cell model can significantly accelerate the discovery of new biological insights and therapies, making AssayBench a vital tool for the scientific community.