arXiv:2606.30695v1 Announce Type: cross Abstract: Single-cell drug perturbation models should predict not only transcriptional response magnitude, but also whether a treatment alters the proliferative state of a cell. This is challenging because cell-cycle variation is often treated as nuisance variation, and benchmark pipelines rarely treat drug-induced phase changes as a primary prediction target. We introduce scCycleMol, a cell-cycle-aware perturbation prediction framework built on a curated 24-hour SciPlex3 benchmark with standardized molecule identities, dose and cell-line metadata, and g
The increasing sophistication of single-cell technologies and AI/ML approaches enables more granular and accurate modeling of biological systems, pushing the boundaries of drug discovery.
This development improves the understanding of drug mechanisms of action at a cellular level, potentially accelerating drug development cycles and leading to more effective and personalized therapies.
Drug perturbation models can now account for cell-cycle variations, moving beyond treating them as noise and leading to more precise predictions of therapeutic effects and potential side effects.
- · Pharmaceutical R&D
- · Biotech companies
- · AI in healthcare
- · Personalized medicine
- · Traditional drug screening methods
- · Companies relying on broad-spectrum drugs
Increased accuracy in predicting drug efficacy and toxicity at the single-cell level.
Faster identification of drug candidates and reduced failure rates in clinical trials, driving down development costs.
The ability to design drugs that specifically target cells in particular phases of their cycle, leading to highly personalized and potent therapies, especially for diseases like cancer.
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Read at arXiv cs.AI