The Foundation for Predictive and Autonomous Drug Discovery:

The Biological Evidence Knowledge Graph (BEKG)

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What is the BEKG?

The Biological Evidence Knowledge Graph (BEKG) is the foundation of BenchSci’s AI-powered solutions. Our knowledge graph unifies vast amounts of disparate biomedical evidence from publications, clinical trials, and datasets into a traceable network of biological insights and relationships. This living map empowers scientists to cut through information overload and fuel AI-powered reasoning, hypothesis generation, and automation in preclinical research.

What makes the BEKG unique

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Engineered for Trust

Built on advanced high-fidelity data extraction, delivering precise, reliable insights without the risk of hallucinations

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Multi-Layered Biological Insights

Correlation, causation, and scientific methodology informed by biomedical evidence, omics datasets and proven protocols and reagents

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Foundation for Scientific Reasoning

Expansive map of biological relationships grounded in ontological knowledge and biomedical research, with every connection traceable back to scientific evidence

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Scientifically Validated

Refined through expert human-in-the-loop curation, ensuring unmatched accuracy, consistency, and confidence in every insight

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Customizable for you

Seamlessly integrates with your internal data to create a proprietary knowledge graph tailored to your organization’s unique needs.

What is our product offering

We turn biological data into discovery, guiding experiments, predictions, and automation through the BEKG.

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Co-pilot: Guiding Experimental Success

ASCEND by BenchSci acts as a co-pilot for preclinical research, combining the BEKG with neuro-symbolic AI to surface relevant evidence, assess project feasibility, and accelerate experiment design. Scientists gain a trusted partner to improve decision-making, boost productivity, and increase experimental success using existing evidence across millions of data sources

Co-scientist: Empowering Scientific Predictions

The BEKG also provides the evidence backbone to make scientific predictions and generate hypotheses like a co-scientist. By applying logic, context, and domain expertise, we can reason and make inferences like a scientist. Together, this co-scientist empowers R&D teams to move from reactive evidence review to proactive discovery by uncovering novel insights, proposing testable hypotheses, and designing optimal studies with the right experiments and best reagents to validate the hypotheses

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A purple line-art icon depicting three horizontal rows of three circular nodes each. The nodes are interconnected by a series of horizontal and diagonal lines, creating a structured lattice or neural network pattern. The icon is centered in a white circle.

Programmatic Access

APIs provide structured access to the BEKG, enabling dedicated AI agents to retrieve permissioned evidence and insights with speed and precision. MCP servers act as universal connectors, allowing AI agents to access tools, APIs, and data (that are strictly authorized or licensed) to orchestrate complex tasks and adapt to diverse scientific use cases. Together, they create a flexible and intelligent ecosystem that is key to the future of AI-powered R&D.

Data Integration

A continuous feedback cycle is key to driving advancement. Evidence-backed hypotheses and strategies flow from the BEKG into automated lab platforms, experiments run autonomously, and results return to the BEKG to refine inferences and strengthen future insights. This continuous cycle accelerates discovery and strengthens predictive power to enhance every step in the R&D process and pave the way toward autonomous drug discovery

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Agentic Lab-in-the-Loop and Process Automation:

Towards Autonomous Drug Discovery

BenchSci’s AI-powered solutions transform and automate repetitive but necessary, evidence-heavy tasks such as literature search, target due diligence, and experiment planning. By embedding automation across preclinical R&D, scientists can focus on innovation and breakthrough science, while organizations accelerate timelines, reduce costs, and lay the foundation for a faster, more effective, and more impactful future of life-saving research.