TxBench-PP benchmark shows leading AI agents achieve at most 59% success on tasks requiring recovery of preclinical pharmacology conclusions from assay data.
Corsello, David D
5 Pith papers cite this work, alongside 3,695 external citations. Polarity classification is still indexing.
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Pith papers citing it
3,695
external citations · OpenAlex
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Drug-blind cancer sensitivity prediction is limited by evaluation metric and training distribution rather than drug representation complexity.
CellScientist introduces a dual-space hierarchical orchestration system that enables closed-loop refinement of virtual cell models by routing execution discrepancies back to hypothesis or implementation updates, yielding improved benchmark performance with auditable traces.
Closed-loop cell-cycle supervision on predicted treated expression raises SciPlex3 phase accuracy by 0.54–0.62 points while keeping all-gene R² within 0.003 of matched chemCPA baselines.
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.
citing papers explorer
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TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
TxBench-PP benchmark shows leading AI agents achieve at most 59% success on tasks requiring recovery of preclinical pharmacology conclusions from assay data.
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Training distribution determines the ceiling of drug-blind cancer sensitivity prediction
Drug-blind cancer sensitivity prediction is limited by evaluation metric and training distribution rather than drug representation complexity.
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CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models
CellScientist introduces a dual-space hierarchical orchestration system that enables closed-loop refinement of virtual cell models by routing execution discrepancies back to hypothesis or implementation updates, yielding improved benchmark performance with auditable traces.
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Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses
Closed-loop cell-cycle supervision on predicted treated expression raises SciPlex3 phase accuracy by 0.54–0.62 points while keeping all-gene R² within 0.003 of matched chemCPA baselines.
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Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
A pathway-constrained autoencoder extended to multi-omics integration improves breast cancer stratification and provides interpretable pathway activity scores.