LLM-Guided Retrieval for Prediction of Molecular Perturbation Responses
2026-08-03 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a method to predict how cells change their gene activity after being treated with new drugs, without testing every drug directly. They use a large language model (LLM) to find similar drugs that have already been tested in the same cell type and then average their effects to predict the new drug's effect. Their method, called LLM-Guided Retrieval (LGR), worked better than other approaches, especially when predicting responses in new cell types. This shows that smart selection of related drugs is more important than complex models for these predictions.
transcriptomicssmall-molecule perturbationcell lineslarge language models (LLM)drug response predictionretrieval methodssingle-cell atlaszero-shot learninggene regulationaggregation
Authors
Betty Xiong, Jan-Christian Huetter, Gabriele Scalia, Tommaso Biancalani, Sepideh Maleki
Abstract
Predicting transcriptomic responses to small-molecule perturbations across cell lines is central to drug discovery, but exhaustive profiling of drug-cell combinations is infeasible. We frame molecular perturbation prediction as retrieve-and-aggregate: approximate an unmeasured drug's response in a cell line by aggregating measured responses of a small set of biologically related compounds. We propose LLM-Guided Retrieval (LGR), where a large language model (LLM) ranks candidate neighbor drugs (restricted to those profiled in the target cell line); after which a fixed mean aggregator combines their observed expression deltas to form the prediction. We evaluate on the Tahoe-100M single-cell perturbation atlas under unseen-drug, unseen-cell-line, and open-world regimes. LGR consistently improves over drug mean, ChemCPA, and chemistry-based kNN baselines, with the strongest gains for unseen cell-line generalization, where it achieves higher correlation and lower error than mean baselines. Across settings, LGR improves directional (sign) accuracy of gene regulation, indicating better recovery of biologically meaningful perturbation effects even when magnitude-based metrics are similar. These results suggest that retrieval quality, rather than predictor complexity, is a key driver of zero-shot molecular perturbation prediction, and that LLMs can provide a useful biological prior when used as constrained retrieval modules.