About
BenchSci is a world leader in AI solutions for preclinical research and development, focused on solving one of drug discovery's hardest problems: decoding the underlying biology of disease. Despite vast amounts of scientific data available today, over 90% of clinical trials fail—largely because R&D teams lack a deep, unified understanding of disease mechanisms. BenchSci's ASCEND platform is the first neuro-symbolic AI system purpose-built to change this. At the heart of ASCEND is the Biological Evidence Knowledge Graph (BEKG), a rigorously curated data backbone that structures over 400 million biological entities and one billion relationships, each linked to traceable experimental sources. The platform ingests tens of millions of scientific publications—including closed-access journals through direct publisher agreements—along with multi-omics data, clinical trials, and clients' proprietary internal data such as lab notebooks. BenchSci offers AI copilots and co-scientists that help researchers go from hypothesis to successful experiment in days rather than years. A dedicated team of over 100 scientists provides human-in-the-loop curation to minimize AI hallucinations and ensure scientific validity. The platform integrates seamlessly into existing biopharma workflows, providing a personalized, secure, and enterprise-grade experience tailored to each organization's unique data landscape. BenchSci is ideal for biopharma R&D teams, drug discovery scientists, and computational biologists aiming to reduce costs and increase the success rate of preclinical programs.
Key Features
- ASCEND Neuro-Symbolic AI Platform: The first neuro-symbolic AI platform designed to unravel disease biology by combining structured knowledge graphs with advanced foundation models for deeper scientific reasoning.
- Biological Evidence Knowledge Graph (BEKG): A rigorously curated knowledge graph with 400M+ biological entities and 1B+ relationships, each traceable to experimental evidence, minimizing AI hallucinations and ensuring data quality.
- Unparalleled Data Access: Integrates tens of millions of publications (including closed-access journals), multi-omics datasets, clinical trial data, and clients' proprietary internal data into a unified knowledge base.
- AI Copilots & Co-Scientists: Provides intelligent research assistants that help scientists accelerate hypothesis generation, experimental design, and data synthesis—cutting discovery timelines from years to days.
- Human-in-the-Loop Curation: A team of 100+ domain scientists continuously curates and validates the knowledge graph to ensure scientific accuracy and consistency across all data sources.
Use Cases
- Biopharma R&D teams using AI to identify disease mechanisms and reduce blind spots in drug discovery programs.
- Drug discovery scientists accelerating hypothesis generation and experimental design by leveraging a unified biological knowledge graph.
- Pharmaceutical companies integrating internal lab data with published literature to create a proprietary, AI-powered research knowledge base.
- Preclinical researchers reducing irreproducible findings by grounding experiments in evidence-backed biological data.
- Computational biologists analyzing multi-omics and clinical trial data alongside scientific publications to uncover novel disease insights.
Pros
- Evidence-Backed AI with Reduced Hallucinations: Every insight in the BEKG is linked to traceable experimental sources and validated by expert scientists, making outputs significantly more reliable than generic AI models.
- Comprehensive & Proprietary Data Integration: Uniquely combines licensed closed-access journals, public data, and client-specific internal data, providing a breadth of information unavailable on competing platforms.
- Tailored to Biopharma Workflows: Designed to integrate into existing R&D processes rather than replace them, thinking like a scientist and supporting how biopharma teams already work.
- Accelerates Drug Discovery Timelines: Helps teams move from hypothesis to successful experiment in days instead of years, dramatically reducing wasted spend on failed programs.
Cons
- Enterprise-Focused Pricing: Designed for large biopharma organizations, making it potentially inaccessible or cost-prohibitive for academic labs or early-stage startups.
- Specialized Use Case: Primarily built for preclinical drug discovery and disease biology research, limiting its applicability to teams outside the biopharma or life sciences space.
- Requires Onboarding & Data Integration: Maximizing platform value requires ingesting proprietary internal data, which demands time, technical resources, and organizational coordination.
Frequently Asked Questions
What is ASCEND by BenchSci?
ASCEND is BenchSci's flagship neuro-symbolic AI platform designed to help biopharma organizations understand disease biology. It combines a curated Biological Evidence Knowledge Graph (BEKG) with advanced foundation models and AI copilots to accelerate hypothesis-to-experiment workflows.
What data sources does BenchSci integrate?
BenchSci integrates tens of millions of scientific publications (including closed-access journals via direct publisher agreements), multi-omics data, clinical trials, and clients' proprietary internal data such as lab notebooks—all unified in a single knowledge base.
How does BenchSci reduce AI hallucinations?
Through its Biological Evidence Knowledge Graph, every piece of information is linked to traceable experimental sources. Additionally, a team of 100+ scientists provides ongoing human-in-the-loop curation to validate accuracy and ensure scientific consistency.
Who is BenchSci designed for?
BenchSci is built for biopharma R&D teams, drug discovery scientists, and computational biologists at life sciences companies who need to accelerate preclinical research and reduce the cost of failed programs.
Can BenchSci incorporate our organization's internal data?
Yes. BenchSci can ingest proprietary internal data—such as lab notebooks and internal datasets—to create a customized, secure map of disease biology tailored specifically to your organization's research programs.
