Integrate trusted scientific data in your AI ecosystem with Elsevier connectors

The connectors work with any MCP compatible agent, including Claude, Copilot, Codex, ChatGPT, KNIME and Pipeline Pilot.

Connectors are an add-on to an existing or new dataset license — Reaxys, ScienceDirect journals or PharmaPendium — not a standalone product. Connectors work in conjunction with your Elsevier dataset to deliver structured, source-linked evidence inside the AI tools your team already uses, resolving identity, harmonizing units and preserving provenance and entitlements on every call.

Every value a connector returns carries its provenance and links back to the source record for verification. Access is governed by ISO 27001 and ISO 27701 aligned controls, using OAuth2, least-privilege access, short-lived tokens and audit trails. Every call respects your organization's licensed entitlements and identity policy, so outputs stay reviewable and audit-ready as AI moves from pilot into production research workflows.

The Journals connector makes full-text scientific literature from ScienceDirect callable inside your AI tools. It contains three tools:

• Candidate retrieval by natural-language query

• Discovery of searchable fields to refine that search

• Retrieval of a full document by identifier

Results are source-linked, ranked and entitlement-aware, so literature review happens inside your existing workflow instead of as a separate search step.

The Chemistry connectors resolve a chemical name, synonym, CAS or registry number, SMILES string, MOL file or systematic IUPAC name, returning a canonical record — SMILES, InChIKey, MOL, Reaxys ID, CAS registry number, synonyms, provenance metadata and structurally related compounds. They run name-to-structure, structure-to-name and TERMite entity recognition, alongside exact, substructure and similarity search.

Connectors help R&D teams:

• Know whether an incoming compound is novel, published or already approved before they spend budget on synthesis or assays

• Ensure SAR series and prediction models are built on clean, deduplicated records so design decisions rest on a true activity signal

• Explore published precedents for routes and catalysts

• Start synthesis planning from precedented routes with known reagent availability

• Provide traceable answers, so results can be audited, reused and defended

The Chemistry Substances connector resolves compound identity across systems, so you can confirm you're working with the right chemistry. It supports exact, substructure and similarity search to help you find relevant structures fast. It returns substance properties alongside each result, giving you the context to make decisions without leaving your workflow. It also surfaces commercial substances and suppliers, so you can move from identification to sourcing in one step.

The Chemistry Reactions connector retrieves reaction data by structure, conditions or catalysts. It retrieves and resolves named reactions, helping chemists, process engineers, pharmacologists and predictive toxicologists find relevant routes and precedent without leaving their AI tool.

The Chemistry Bioactivity connector resolves target identity, supports target-centric compound search, and retrieves bioactivity data linked to those targets and compounds, bringing target and compound evidence together inside your AI workflow. Bioactivity data includes mechanism of action, compound prioritization and competitive landscape mapping.

The Drug Safety connector retrieves safety data from literature and global regulatory precedents by drug, adverse effect, species or test system, and dose or route, with source traceability carried through every result. The Drug Safety connector gives toxicologists and safety scientists a consistent, citable view across studies.

The Pharmacokinetic connector retrieves PK data from literature and global regulatory precedents by drug, species or test system, and dose or route, harmonizing units and study context automatically and preserving source traceability. The Pharmacokinetic connector helps DMPK and clinical pharmacology teams compare data across studies faster, without a manual harmonization step.