PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction
arXiv:2606.27752v1 Announce Type: new Abstract: Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. While recent generative models improve population-level prediction, individual generated cells are not explicitly checked for biological consistency. We introduce PerturbCellRL, a reinforcement learning (RL) framework that post-trains a pretrained single-cell transcriptomic generator using a suite of cell-level verifiers as rewards. These verifiers define four rewards: Pearson top-k similarity, RMSE top-k proximi
The increasing sophistication of generative AI models in biology is enabling more precise control and verification of their outputs, moving beyond population-level predictions to individual cellular responses.
This development allows for significantly reduced reliance on costly and time-consuming wet-lab experiments by providing more accurate and biologically consistent in-silico predictions of cellular perturbations.
The ability to post-train and verify generated cells for biological consistency through RL will accelerate drug discovery, materials science, and fundamental biological research, making in-silico experimentation more reliable.
- · Biopharmaceutical companies
- · Synthetic biology researchers
- · AI-driven drug discovery platforms
- · Precision medicine developers
- · Traditional wet-lab screening services
Accelerated discovery of novel therapeutics and engineered biological systems due to more efficient in-silico screening.
Reduced R&D costs and shortened timelines for bringing new biological products to market.
The development of 'digital twins' for complex biological systems, allowing for highly predictive and personalized interventions.
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Read at arXiv cs.LG