Chem-PerturBridge: a harmonized compendium of small molecule perturbation transcriptomic effects
arXiv:2605.31522v1 Announce Type: new Abstract: Large perturbation models require training data encompassing chemical, cellular, and assay diversity. Current transcriptomic resources for small-molecule modeling, however, are fragmented across technologies, metadata conventions, controls, doses, and preprocessing pipelines. We introduce Chem-PerturBridge, a harmonized multi-dataset resource comprising over 37k compounds, 136 cellular contexts, and 1.25M transcriptomic samples across eight assay types, with standardized identifiers, metadata, and replicate-aware condition-level effects. We use t
The proliferation of AI and advanced computational methods necessitates harmonized, large-scale biological datasets to accelerate drug discovery and synthetic biology applications.
A standardized and massive compendium of transcriptomic effects from small molecules creates a foundational resource for AI-driven drug discovery, accelerating target identification and lead optimization.
Fragmented and inconsistent small-molecule perturbation data is being unified into a single, accessible resource, enabling more robust and scalable AI models for biotech.
- · AI drug discovery startups
- · Pharmaceutical companies
- · Synthetic biology research
- · Biotech data platforms
- · Traditional drug discovery methods
- · Fragmented bioinformatics pipelines
Researchers gain access to a vastly improved dataset for training predictive models of drug-gene interactions.
The cost and time required for early-stage drug discovery and lead compound identification are significantly reduced.
Accelerated development of novel therapeutics and advanced biological materials, potentially leading to new biotech industries.
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Read at arXiv cs.LG