AI Services for Every Stage of Discovery
Modular or end-to-end, our AI platform integrates at any point in your drug discovery pipeline.
Explore Services
Target Discovery & Validation
Identifying the right biological target is the single most critical decision in drug development.
A misidentified or poorly validated target accounts for the majority of late-stage attrition.
Prognica Labs uses a multi-layered AI approach – integrating genomic, transcriptomic, proteomic, and clinical data – to identify high-confidence, mechanistically justified targets with therapeutic relevance.
Reduced false-positive target selection by up to 60%
Target validation in 6–8 weeks vs. 12–18 months conventionally
Mechanistic rationale for each target, regulatory documentation ready
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Key Capabilities
Multi-omics target scoring across 10+ disease-relevant datasets
CRISPR genetic screen data integration for functional validation
Network perturbation analysis and pathway modeling
Target druggability and tractability assessment
Competitive landscape and IP freedom-to-operate overlay
Tissue and cell-type expression specificity analysis
Generative Hit Identification
Once a validated target is in hand, generating hit compounds with the right selectivity, potency, and chemical properties is the next bottleneck. Prognica Labs uses generative chemistry AI, trained on 120M+ compounds and proprietary bioactivity datasets, to propose novel, synthesizable hits that outperform traditional HTS in hit quality and diversity.
Typical hit rate: 12–18% vs. 0.01–0.1% in HTS
Reduced screening cost by up to 70%
Novel scaffolds with freedom-to-operate in target IP landscape
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Key Capabilities
Generative molecular design using transformer and diffusion models
Virtual screening across 10B+ compound libraries
De novo scaffold generation for novel IP spaces
Selectivity profiling against off-target panels
Synthetic accessibility scoring and retrosynthetic pathway prediction
Clustered hit-set delivery with diversity analysis
Lead Optimization (AI-Guided SAR)
Iterative lead optimization is where most discovery programs spend the majority of their time and resources. Prognica Labs replaces slow, hypothesis-driven medicinal chemistry cycles with AI-guided SAR loops, dramatically compressing the time to identify a development candidate with the right efficacy, selectivity, and drug-like properties.
Development candidate identification in 4–6 months vs. 18–24 months
2–3× improvement in multi-parameter optimization success rate
Full documentation package for regulatory submission
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Key Capabilities
Multi-parameter SAR modeling across potency, selectivity, and ADMET
Bayesian optimization for multi-property compound design
De novo scaffold generation for novel IP spaces
Active learning loop integrating wet-lab feedback in real time
Matched molecular pair analysis for rapid analog design
Automated synthetic route proposal with commercially available reagents
ADMET & Safety Prediction
Late-stage attrition due to ADMET (absorption, distribution, metabolism, excretion, toxicity) failures accounts for >40% of clinical failures and billions in wasted investment. Prognica Labs’ ADMET engine delivers >85% predictive accuracy on key endpoints, enabling early de-risking before costly in vivo work begins.
Reduced late-stage attrition due to ADMET failures
In silico ADMET panels reduce in vitro screening cost by 40%
FDA-ready prediction reports with confidence intervals
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Key Capabilities
>85% accuracy across 40+ ADMET endpoints
hERG, CYP inhibition, and drug-drug interaction prediction
Blood-brain barrier penetration and CNS PK modeling
Hepatotoxicity, genotoxicity, and cardiotoxicity flags
Plasma protein binding and metabolic stability prediction
Species-to-species PK translation models
Biomarker Intelligence & Patient Stratification
Precision medicine requires matching the right drug to the right patient. Prognica Labs’ biomarker platform uses AI-driven multi-omics analysis to identify predictive, prognostic, and pharmacodynamic biomarkers, enabling smarter clinical trial design and faster regulatory approval through biomarker-led enrollment strategies.
Trial enrollment efficiency increased by 2–3×
Companion diagnostic co-development timelines reduced by 40%
Regulatory-grade biomarker analysis packages for FDA/EMA submissions
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Key Capabilities
Predictive biomarker identification from clinical genomics data
Companion diagnostic development support
Patient stratification algorithms for adaptive trial design
Multi-omics biomarker panels (genomic, proteomic, metabolomic)
Tumor microenvironment and immune profiling AI
Real-world data integration for biomarker validation
End-to-End CRO Partnership
For partners who want full discovery program ownership with AI at every stage, Prognica Labs offers an integrated CRO model — taking scientific accountability from target selection through IND-enabling studies. We embed our team with yours, sharing risk and rewards through flexible partnership structures.
IND filing in 24–30 months vs. industry average of 48–60 months
Milestone-based payments aligned with scientific de-risking
Multiple programs supported in parallel with modular team scaling
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Key Capabilities
Full program management from target to IND
Dedicated computational and experimental teams
Risk-sharing, milestone, and fee-for-service engagement models
Regulatory strategy and IND preparation support
Monthly scientific board reviews with full data transparency
IP co-ownership options under risk-sharing agreements
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