AI-Powered Drug Discovery Platform
From Fragmented Data
From Fragmented Data
to High-Confidence Drug Targets
Bio-Graph is an AI-powered knowledge graph platform that connects the entire biomedical universe — accelerating target identification, drug repurposing, and clinical prioritization for biotech and pharma teams.
Why Bio-Graph
Built for the Complexity of Drug Discovery
Bio-Graph compresses years of manual research into actionable, evidence-backed insights — so your team can focus on decisions, not data wrangling.
Unified Biomedical Knowledge
All major knowledge domains — genes, proteins, pathways, diseases, compounds — connected in one traversable graph.
AI-Augmented Evidence Mining
Intelligent agents continuously extract novel connections from publications, patents, and structured databases.
Configurable Target Prioritization
Rank targets against your own criteria — druggability, safety signals, novelty, and competitive landscape — in one workflow.
Full Provenance & Explainability
Every inference is traceable to its source with confidence scores — enabling trust, reproducibility, and regulatory documentation.
Real-Time Graph Enrichment
Automated pipelines and human-in-the-loop review keep the knowledge graph current and continuously improving.
Drug Repurposing at Scale
Identify new indications for approved compounds by mapping molecular mechanisms across disease ontologies and clinical evidence.
Use Cases
Accelerate Clinical Research at Every Stage
Bio-Graph addresses the three most critical bottlenecks in early drug discovery — all in one platform.
New Target Identification
Traverse multi-hop relationships across genes, variants, pathways, and phenotypes to surface novel high-confidence targets invisible to conventional approaches. Quickly generate and assess new hypotheses to guide R&D strategy.
Drug Repurposing
Map existing approved compounds onto newly identified mechanistic pathways. Leverage highly interlinked information across data sources to discover new indications at dramatically reduced cost and clinical risk.
Target Prioritization
Score and rank candidate targets against multi-criteria frameworks — druggability, safety signals, clinical translatability, and pipeline fit. Data-driven decision-making with transparent scoring and customizable ranking options.
Platform Architecture
Bio-Graph in Detail
Three deeply integrated layers — from knowledge ingestion to intelligent prioritization — form the foundation of the Bio-Graph platform.
Multimodal Knowledge Graph
A comprehensive graph spanning every major knowledge domain relevant to drug discovery — integrating structured databases and unstructured literature into a single, traversable resource.
- Genes, variants, and functional annotations
- Proteins, structures, and interaction networks
- Biological pathways and regulatory mechanisms
- Disease ontologies and phenotypic associations
- Compounds, clinical trials, and adverse events
Knowledge Enrichment via Intelligent Agents
Continuously expanding knowledge coverage through automated multi-source extraction — with full provenance tracking and expert oversight built in at every step.
- NLP and LLM-driven literature and patent mining
- Automated integration with leading biomedical DBs
- Confidence scoring and evidence provenance
- Human-in-the-loop expert review and annotation
- Incremental graph updates without downtime
Configurable Criteria Prioritization Agent
A flexible, AI-powered scoring engine that ranks drug candidates against criteria you define — adapting to your research strategy, therapeutic area, and business priorities.
- Multi-criteria weighted scoring models
- Druggability, safety, and clinical translatability
- Custom algorithm integration via open API
- Transparent ranking with full audit trail
- Interactive dashboards for decision review
Ready to Discover Your Next Breakthrough Target?
Join leading biotech and pharma teams using Bio-Graph to compress years of research into weeks. See the platform live — tailored to your pipeline and therapeutic area.
Try Bio-Graph →
Who Benefits
Built for Every Team Advancing Life Sciences
Bio-Graph adapts to the needs of research-driven organizations — from agile biotech startups to global pharmaceutical enterprises and leading academic institutes.
Biotech Companies
Maximize limited R&D resources by rapidly identifying high-confidence targets and repurposing opportunities. Move faster from hypothesis to validated candidate with AI-guided insights and transparent evidence trails.
Pharma Companies
Integrate Bio-Graph into existing drug discovery pipelines to semantically connect proprietary data with public biomedical knowledge — unlocking hidden relationships and driving data-driven portfolio decisions at scale.
Research Institutes
Empower scientists to navigate vast biomedical knowledge without technical barriers. Surface relevant connections across EMBL, PubMed, and proprietary datasets in minutes, not months.
The Team
Meet the Experts Behind Bio-Graph
A multidisciplinary team combining deep AI research, biomedical data engineering, and decades of pharma industry experience.
Svetla Boytcheva
AI Expert & Research Lead
PhD in Computer Science specialising in AI, ML, and NLP. Formal certifications across core AI governance frameworks and 15+ years leading AI research and delivering real-world solutions in healthcare and life sciences.
Plamen Tarkalanov
Data Engineer
10+ years designing and scaling data platforms, currently focused on integrating heterogeneous biomedical data and applying LLMs for entity recognition and knowledge extraction.
Boyan Simeonov
Software Architect
13+ years building and leading high-performing engineering teams, software and system architectures, and cloud-native systems across healthcare, life sciences, and AI-driven platforms.
Todor Primov
Pharma & AI Strategy
25+ years leading AI innovation in pharma, with a strategic focus on leveraging complex bio-medical data to build scalable, commercially viable solutions in pharma, biotech, and healthcare.
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