Chem2Gen-Bench is a new benchmark and evaluation framework for measuring alignment between chemical and genetic perturbation responses in matched cell-target contexts using retrieval, similarity, and embedding comparisons.
Predicting cellular responses to complex perturbations in high-throughput screens.Molecular Systems Biology, 19(6):e11517, 2023
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OCOO-T is a flow-matching Transformer model that directly denoises continuous gene expression profiles to predict transcriptional responses to perturbations and reports state-of-the-art results on Tahoe100M, Replogle, and PBMC benchmarks.
Under Bemis-Murcko scaffold split on THP-1 DRUG-seq data, inverse-variance proxy ranks linear Morgan fingerprint regression highest while contest wMSE ranks deep fusion models highest, with fusion beating linear by -0.012 wMSE (p<10^-4).
K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while improving downstream differential expression prediction.
citing papers explorer
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Chem2Gen-Bench: Benchmarking Chemical-to-Genetic Translation in Perturbation Response Space
Chem2Gen-Bench is a new benchmark and evaluation framework for measuring alignment between chemical and genetic perturbation responses in matched cell-target contexts using retrieval, similarity, and embedding comparisons.
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OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction
OCOO-T is a flow-matching Transformer model that directly denoises continuous gene expression profiles to predict transcriptional responses to perturbations and reports state-of-the-art results on Tahoe100M, Replogle, and PBMC benchmarks.
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The Metric Picks the Winner: Evaluation Choice Flips Model Rankings for Drug-Response Prediction in Unseen Chemistry
Under Bemis-Murcko scaffold split on THP-1 DRUG-seq data, inverse-variance proxy ranks linear Morgan fingerprint regression highest while contest wMSE ranks deep fusion models highest, with fusion beating linear by -0.012 wMSE (p<10^-4).
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Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors
K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while improving downstream differential expression prediction.