What does this research mean for the field?

Equipping LLM agents with a structured knowledge graph and Model-Context-Protocol (MCP) tools significantly improves the retrieval of valid biomedical identifiers and provides structured, auditable evidence for pre-clinical R&D compared to baseline models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This work aims to address the fragmentation of pre-clinical R&D intelligence and improve target selection for therapies.

June 7, 2026Open Access

Mosaic: a knowledge-graph + MCP-tool layer for pre-clinical R&D intelligence

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Authors

SNSourabh Nyalkalkar

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Implication

Randomized trial demonstrates improved target identification in pre-clinical research, highlighting enhanced decision-making processes.

Key Points

This work aims to address the fragmentation of pre-clinical R&D intelligence and improve target selection for therapies.
Developed Mosaic, an MCP tool over a knowledge graph using Postgres, Apache AGE, and pgvector.
Incorporated zero-shot GLiREL for relation extraction and Foldseek for structural similarity.
Evaluated performance on benchmark questions and case studies with a focus on structured outputs.
Mosaic cites four times as many valid identifiers compared to a no-tool baseline; zero invalid IDs in either arm.
Achieved a 33% win-rate across twelve benchmark questions, improved to ~58% post-error correction by human judges.
Demonstrated consistent advantages in structured output quality across five case studies.

Cite This Study

Sourabh Nyalkalkar (2026) studied this question.

synapsesocial.com/papers/6a250ae37def13d035e1afa9 https://doi.org/10.5281/zenodo.20557286

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Mosaic: a knowledge-graph + MCP-tool layer for pre-clinical R&D intelligence

Discussion

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Implication

Key Points

Cite This Study

Mosaic: a knowledge-graph + MCP-tool layer for pre-clinical R&D intelligence

Discussion

Member takes

Implication

Key Points

Cite This Study

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