Researchers introduced Scouter, a tool that leverages large language models to predict transcriptional changes arising from genetic perturbations. Published in Nature Computational Science, the work by Zhu and Li demonstrates LLM‑based translation from genetic perturbation inputs to expected transcriptional outputs, bridging sequence perturbations and cellular response prediction. Scouter aims to accelerate hypothesis generation for functional genomics and target validation by forecasting gene expression consequences of edits or perturbations without immediate wet‑lab experiments. For drug discovery, such in silico forecasting could prioritize edits for experimental follow‑up and reduce early‑stage screening burdens. The authors note that while LLMs can model complex sequence-to-function mappings, their outputs still require experimental validation; Scouter is positioned as a decision‑support tool rather than a substitute for empirical assays.