Why K Pro?
How K Pro differs from other AI-for-target-prioritization platforms, and the outcomes it is designed to improve.
This page explains how K Pro differentiates from other platforms in the AI-for-target-prioritization space. For an overview of what K Pro does, see What is K Pro?.
How K Pro differentiates
Multimodal patient data at depth. Most platforms sit on top of aggregated public databases (Open Targets, GWAS Catalog, ChEMBL). K Pro is built on Owkin's data network of 164 academic medical institutions, with MOSAIC as the flagship cohort in oncology: one of the largest spatially-resolved multimodal oncology datasets, combining bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics, whole-exome sequencing, H&E and IHC pathology imaging, and longitudinal clinical follow-up. For target prioritization, this enables spatial co-localization of targets with TLS structures, malignant-cell specificity at single-cell resolution, intra-tumor heterogeneity quantification.
Specialized AI models, wrapped in expert skills. K Pro orchestrates calls to specialized models like HistoPLUS (Owkin's computational pathology foundation model, Adjadj et al., 2025), which detects 13 cell types including under-studied immune populations like neutrophils and eosinophils, with 14% better F1 classification than prior SOTA at 5× fewer parameters. It can also call Owkin Zero, our proprietary biological reasoning model (32B-parameter post-trained model on ~10M curated Q&A pairs with reinforcement learning from verifiable biological rewards). These models are called by skills encoding specialized know-how — for example, how to orchestrate relevant data and tools to produce a target characterization package consistent with customer templates. Skills are provided by Owkin and can be customized per team.
Outcomes K Pro is designed to improve
For target prioritization specifically, K Pro is designed to improve several measurable outcomes:
Time to first target dossier (literature + expression + competitive landscape): 2–4 weeks of analyst time before K Pro, vs. hours with K Pro, reviewed and refined collaboratively
Number of targets evaluated in parallel for a given indication: a handful before (constrained by analyst capacity), vs. dozens in parallel and consistent campaigns with K Pro
Reproducibility of scoring across analysts and projects: same skill applied consistently with versioned cohort definitions
Evidence coverage (modalities per target): often bulk RNA-seq plus literature before, vs. bulk + single-cell + spatial RNA + pathology + clinical landscape in addition to literature with K Pro
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