LLM-Guided Retrieval for Prediction of Molecular Perturbation Responses

• URL: http://arxiv.org/abs/2608.01734v1
• Date: Mon, 03 Aug 2026 05:57:02 GMT
• Title: LLM-Guided Retrieval for Prediction of Molecular Perturbation Responses
• Abstract summary: We frame molecular perturbation prediction as retrieve-and-aggregate.<n>A large language model (LLM) ranks candidate neighbor drugs (restricted to those profiled in the target cell line)<n>A fixed mean aggregator combines their observed expression deltas to form the prediction.<n>We evaluate on the Tahoe-100M single-cell perturbation atlas under unseen-drug, unseen-cell-line, and open-world regimes.
• Score: 9.104983140706517
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy [8.012730274764019]
  Accurate molecular property prediction requires both statistical reliability and chemical reasoning.<n>CoMPASS is a retrieval-calibrated framework for small-large model collaboration.<n>Results suggest that generative reasoning should augment calibrated prediction through evidence-grounded, controlled corrections.
  arXiv  Detail & Related papers  (2026-08-31T12:14:01Z)
• BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells [64.21963170814706]
  BioM-JEPA predicts aggregate representations of graph-connected gene blocks.<n>A student network infers each target-block representation from the remaining genes in a cell.<n>Across CellBench tasks, BioM-JEPA embeddings retained expression, pathway and neighbourhood information.
  arXiv  Detail & Related papers  (2026-08-06T11:58:29Z)
• Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction [3.229566379058554]
  We show that training a lightweight alignment module atop frozen histopathology and RNA-Seq foundation models enables open-vocabulary molecular prompting.<n>We achieve a 25-fold improvement in retrieval over baseline methods.<n>We validate clinical utility on the POSEIDON clinical trial.
  arXiv  Detail & Related papers  (2026-06-29T08:24:53Z)
• Plausibility Is Not Prediction: Contrastive Evidence for LLM-Based Cellular Perturbation Reasoning [27.902219493068824]
  Perturbation experiments are central to understanding cellular mechanisms.<n>They remain costly and sparse, motivating prediction of gene expression responses for unobserved conditions.<n>We introduce CORE, which reframes prediction as a comparison task by organizing evidence into positive and negative outcomes from related perturbations.
  arXiv  Detail & Related papers  (2026-05-31T06:13:26Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation [51.96735866702332]
  PREBA is a retrieval-augmented framework that integrates PCA-weighted retrieval and Bayesian averaging aggregation.<n> PREBA significantly improves performance-for instance, reducing MAE by up to 40% and raising R2 from -0.13 to 0.62 over zero-shot inference.
  arXiv  Detail & Related papers  (2026-02-27T07:19:23Z)
• Latent Causal Diffusions for Single-Cell Perturbation Modeling [83.47931153555321]
  We present a generative model that frames single-cell gene expression as a stationary diffusion process observed under measurement noise.<n> LCD outperforms established approaches in predicting the distributional shifts of unseen perturbation combinations in single-cell RNA-sequencing screens.<n>We develop an approach we call causal linearization via perturbation responses (CLIPR), which yields an approximation of the direct causal effects between all genes.
  arXiv  Detail & Related papers  (2026-01-20T16:15:38Z)
• Transcriptome-Conditioned Personalized De Novo Drug Generation for AML Using Metaheuristic Assembly and Target-Driven Filtering [0.0]
  Acute Myeloid Leukemia (AML) remains a clinical challenge due to its extreme molecular heterogeneity and high relapse rates.<n>This paper presents a novel, end-to-end computational framework that bridges the gap between patient-specific transcriptomics and de novo drug discovery.
  arXiv  Detail & Related papers  (2025-12-24T17:39:37Z)
• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)
• Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data [0.0]
  Bi-Directional Long Short Term Memory (BiLSTM) is a variant of Recurrent Neural Network (RNN) that processes input molecular sequences. The proposed work aims to understand the sequential patterns encoded in the SMILES strings, which are then utilised for predicting the toxicity of the molecules.
  arXiv  Detail & Related papers  (2024-07-08T18:12:11Z)
• Regressor-free Molecule Generation to Support Drug Response Prediction [83.25894107956735]
  Conditional generation based on the target IC50 score can obtain a more effective sampling space. Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels.
  arXiv  Detail & Related papers  (2024-05-23T13:22:17Z)
• CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data [61.088705993848606]
  We introduce CausalBench, a benchmark suite for evaluating causal inference methods on real-world interventional data. CaulBench incorporates biologically-motivated performance metrics, including new distribution-based interventional metrics.
  arXiv  Detail & Related papers  (2022-10-31T13:04:07Z)
• A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction [1.0128447823756244]
  We present VADEERS, a Variational Autoencoder-based Drug Efficacy Estimation Recommender System. The generation of compounds is performed by a novel variational autoencoder with a semi-supervised Gaussian Mixture Model (GMM) prior. VADEERS offers a comprehensive model of drugs and cell lines properties and relationships between them, as well as a guided generation of novel compounds.
  arXiv  Detail & Related papers  (2022-06-07T20:13:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.