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ARXIV:2603.17380 · BIOLOGICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.17380BIOLOGICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
SCALE is a specialized model for predicting virtual cell responses to perturbations, enhancing both speed and biological accuracy.
Opportunity summary
Pain SCALE is a specialized model for predicting virtual cell responses to perturbations, enhancing both speed and biological accuracy.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
SCALE is a specialized model for predicting virtual cell responses to perturbations, enhancing both speed and biological accuracy. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference…
Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements.
Biological AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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SCALE is a specialized model for predicting virtual cell responses to perturbations, enhancing both speed and biological accuracy.
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10.48550/arXiv.2603.17380SCALE is a specialized model for predicting virtual cell responses to perturbations, enhancing both speed and biological accuracy.
Abstract
Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipelines, unstable modeling in high-dimensional sparse expression space, and evaluation protocols that overemphasize reconstruction-like accuracy while underestimating biological fidelity. In this work we present a specialized large-scale foundation model SCALE for virtual cell perturbation prediction that addresses the above limitations jointly. First, we build a BioNeMo-based training and inference framework that substantially improves data throughput, distributed scalability, and deployment efficiency, yielding 12.51* speedup on pretrain and 1.29* on inference over the prior SOTA pipeline under matched system settings. Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture that couples LLaMA-based cellular encoding with endpoint-oriented supervision. This design yields more stable training and stronger recovery of perturbation effects. Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics rather than reconstruction alone. On this benchmark, our model improves PDCorr by 12.02% and DE Overlap by 10.66% over STATE. Together, these results suggest that advancing virtual cells requires not only better generative objectives, but also the co-design of scalable infrastructure, stable transport modeling, and biologically faithful evaluation.
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Dimensions overall score 7.0
PROBLEM
SCALE is a specialized model for predicting virtual cell responses to perturbations, enhancing both speed and biological accuracy. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipel...
METHOD
Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks:...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements.
WHY NOW
Biological AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
SCALE is a specialized model for predicting virtual cell responses to perturbations, enhancing both speed and biological accuracy. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipelines, unstable modeling in high-dimensional sparse expression space, and evaluation protocols that overemphasize reconstruction-like accuracy while underestimating biological fidelity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipelines, unstable modeling in high-dimensional sparse expression space, and evaluation protocols that overemphasize reconstruction-like accuracy while underestimating biological fidelity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Biological AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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SCALE is a specialized model for predicting virtual cell responses to perturbations, enhancing both speed and biological accuracy.
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Biological AI
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