BenchSci
Free tierThe agentic AI workbench that gives every scientist a team of expert PhD scientists for preclinical R&D
Free tier available·Technical·Powered by Multiple frontier models (model-agnostic), plus ESM-2, AbLang2, and RDKit·API available
Key strengths
Access to 16M closed-access papers via exclusive publisher partnershipsProprietary knowledge graph with 858M nodes and 2.2B relationship edges95%+ accuracy via neuro-symbolic evaluation — 2–4x better than frontier LLMs100+ domain-specific scientific skills for target ID, omics, and experiment designCurated reagent data: 16M antibodies, 22M RNAi entries, 18M CRISPR records
Free tier + paid plans
Toronto, Canada
Founded 2015
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Recent changes
- Pricing model: enterprise → freemium
- Plans updated: Free tier for academic, nonprofit, and government organizations (limited access, 24/7 self-serve support). Custom Enterprise pricing for pharma and biotech companies, starting with a free pilot.
- Models updated: Multiple frontier models (model-agnostic), plus ESM-2, AbLang2, and RDKit
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Technical Use Cases
- Reagent selection & validation: Query the knowledge graph across antibodies, RNAi, CRISPR, cell lines, and animal models to identify and validate experimental reagents — backed by curated data and closed-access literature from 16M papers.
- Drug discovery workflow automation: Deploy 100+ proprietary scientific skills to automate multi-step drug discovery tasks, with model-agnostic orchestration routing subtasks to the optimal frontier LLM, ESM-2, AbLang2, or RDKit as appropriate.
- Protein & antibody analysis: Leverage ESM-2 and AbLang2 within the orchestration layer for sequence-level protein and antibody reasoning embedded directly in research workflows.
- Cheminformatics tasks: Integrate RDKit-powered chemical analysis into agentic pipelines for small-molecule drug discovery support.
- IP-aware research: Use the Patsnap integration to cross-reference scientific findings with patent landscapes in a single workflow.
- High-accuracy evidence synthesis: Apply the neuro-symbolic evaluation loop to synthesize evidence from the 858M-node knowledge graph and literature corpus, with 95%+ accuracy guarantees for downstream decision-making.
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