Built for Biological Complexity
A fully integrated, multi-layer AI architecture designed for the biological complexity of modern drug discovery.
Most AI tools in drug discovery address a single stage in isolation. Prognica Labs built a fully integrated platform where each layer informs and amplifies the others — creating compounding intelligence across the entire discovery workflow.
Layer 01
Data Ingestion &
Harmonisation Layer
- Automated ETL pipelines for 50+ public databases (TCGA, GTEx, UniProt, ChEMBL)
- Proprietary data harmonization and ontology mapping
- Real-world evidence integration (EHR, claims data)
- Secure partner data enclave with end-to-end encryption
Layer 02
Multi-Omics AI
Integration Engine
- Knowledge graph with 2B+ biological entity relationships
- Cross-modal attention mechanisms for omics fusion
- Causal inference models for target mechanism elucidation
- Single-cell resolution transcriptomic analysis
Layer 03
Proprietary generative models trained on 120M+ compounds produce novel, synthesizable molecules optimized across multiple drug-like properties simultaneously, moving beyond analog-based medicinal chemistry.
- Transformer and diffusion-based molecular generators
- Conditional generation for specific target profiles
- Automated retrosynthesis and route scoring
- REINVENT-style RL optimization for multi-parameter objectives
Layer 04
Ensemble ML models predict ADMET properties, binding affinity, selectivity, and PK/PD parameters with validated accuracy across >85% of endpoints, reducing in vitro and in vivo screening burden significantly.
- 40+ ADMET predictive models with prospective validation data
- Binding affinity prediction using FEP+ and ML hybrid approaches
- CYP and P450 metabolism models with isoform specificity
- PK/PD modeling for dose regimen optimization
Layer 05
Every prediction in our platform is accompanied by mechanistic rationale – attention maps, feature importance scores, and biological pathway annotations – enabling scientific trust and regulatory confidence.
- SHAP-based feature attribution for every model output
- Attention visualization for molecular and sequence models
- Pathway-level explanation for target and biomarker calls
- Regulatory-grade model cards and audit trails
Layer 06
An intelligent workflow engine connects all platform components into automated, adaptive discovery loops – routing compounds and targets through the right analyses at the right time, minimizing manual handoffs.
- Active learning loops that adapt based on experimental feedback
- Automated compound prioritization and triage
- Real-time dashboard for program tracking and decision support
- API integrations with major ELN, LIMS, and CRO platforms
Contact us with your own compound or disease target for a demo.
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