Our AI Platform 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.
Explore Services
M+
Compounds in training data
B+
Biological entity relationships
+
ADMET predictive endpoints
+
Integrated public databases
Layer 1
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
We ingest and harmonize public and proprietary multi-omics datasets – genomics, transcriptomics, proteomics, metabolomics – along with clinical, structural biology, and literature data through automated curation pipelines.
Layer 2
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
Our flagship integration engine fuses signals across biological modalities using graph neural networks and multi-modal transformer architectures, surfacing emergent biological insights invisible to single-modality analysis.
Layer 3
Generative Chemistry Platform
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
Proprietary generative models trained on 120M+ compounds produce novel, synthesizable molecules optimized across multiple drug-like properties simultaneously, moving beyond analog-based medicinal chemistry.
Layer 4
Predictive Modeling Suite
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
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.
Layer 5
Explainable AI (XAI) Framework
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
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.
Layer 6
Discovery Orchestration & Workflow Automation
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
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.
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