Pioneering hypothesis-free drug discovery through the Interrogative Biology® platform and Causal AI.
BERG, now the core technology platform of BPGbio, represents the frontier of AI in life sciences as of 2026. Its technical architecture is built around the Interrogative Biology® platform, which utilizes a 'hypothesis-free' approach to biological research. Unlike traditional AI models that rely on existing scientific literature (which may contain biases), BERG's Bayesian-based Causal AI engine analyzes raw multi-omics data—including proteomics, lipidomics, and metabolomics—from both healthy and diseased human samples. This allows the system to map the 'interactome' of a cell and identify the actual drivers of disease rather than simple correlations. By 2026, BERG's positioning has shifted from a pure service provider to a strategic clinical-stage partner, leveraging its bAIcis® high-performance computing environment to accelerate drug development in oncology, neurology, and rare diseases. The platform is notable for its ability to create 'Digital Twins' of biological systems, enabling researchers to simulate drug-target interactions with high fidelity before entering wet-lab validation, significantly reducing the 'Eroom's Law' effect in pharmaceutical R&D.
BERG, now the core technology platform of BPGbio, represents the frontier of AI in life sciences as of 2026.
Explore all tools that specialize in identify drug targets. This domain focus ensures BERG (BPGbio) delivers optimized results for this specific requirement.
Explore all tools that specialize in discover biomarkers. This domain focus ensures BERG (BPGbio) delivers optimized results for this specific requirement.
Explore all tools that specialize in accelerate drug discovery. This domain focus ensures BERG (BPGbio) delivers optimized results for this specific requirement.
Explore all tools that specialize in causal ai. This domain focus ensures BERG (BPGbio) delivers optimized results for this specific requirement.
A system-based approach that integrates molecular data from patients with Bayesian AI to identify disease drivers.
High-performance computing architecture designed for massive Bayesian network inference.
Advanced mathematical modeling that identifies the directionality of biological signals.
Simultaneous analysis of genomics, proteomics, lipidomics, and metabolomics.
Access to over 100,000 highly curated human tissue and fluid samples.
Deep analysis of cellular lipids which are critical for signaling and structural integrity.
Machine learning algorithms that group patients by biological signature rather than clinical symptoms.
Strategic therapeutic area alignment and scope definition.
Secure ingestion of multi-omics data into the bAIcis® environment.
Data normalization and quality control using automated pipelines.
Construction of the Bayesian network model representing the biological system.
Execution of the Causal AI engine to identify perturbed nodes.
Rank-ordering of potential drug targets based on biological relevance.
In-silico validation of targets against BPGbio's proprietary biobank.
Generation of a prioritized list of biomarkers for patient stratification.
Integration with wet-lab facilities for functional validation.
Final reporting and transition to IND-enabling studies.
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Verified feedback from other users.
"Extremely highly regarded in the biotech industry for finding 'novel' biology that other AI platforms miss. Users praise the causal aspect over standard deep learning."
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The AI-driven pharmatech company translating patient data into precision medicines through automated molecular design.
Accelerating drug discovery and precision medicine through federated learning and multi-modal AI.
Systematic drug target identification and prioritization through large-scale genomic and clinical data integration.
Unifying life sciences data into an actionable knowledge graph for accelerated drug discovery.
Engineering biology at scale to discover and develop next-generation therapeutics.
Accelerate drug discovery workflows with AI-driven evidence synthesis and biological insight.
