u/Grumintor

I am building a solo product employing knowledge graph architecture to multiple datasets employed in pre-clinical research such as ChemBL, Pubmed, Patents, Opentargets, Depmap, Reactome and more.
So when someone wants answers to complex queries like where are the white spaces in oncology - the knowledge graph returns answers that are better than regular structured searches.
Now to demonstrate the capability, I prepared a set of clinical/biomedical research queries and ran them against a. My knowledge graph architecture + LLM (Claude Sonnet) b. Claude Sonnet with web search

Results: My architecture coupled with LLM was 33% better than the commonly used AI.

I have published these results here: https://zenodo.org/records/20557287

To reach wider audience and validate my approach I want to submit this at Arxiv (cs.CL category) but it requires endorsement from at least one author in the same category. Can anyone help here?

Sharing a project I've been working on. Mosaic is a biomedical knowledge graph for pre-clinical oncology drug discovery — semantic relations extracted from ~35K abstracts via GLiREL (zero-shot relation extraction with DeBERTa-v3-large backbone), joined with structural similarity (AlphaFold + Foldseek), DepMap genetic data, Reactome pathways, and STRING PPIs.

The extraction precision question — GLiREL gives a macro-F1 of 39.5% across 13 relation types on a 500-row human-reviewed sample. Better on binding-family relations where ChEMBL provides ground truth (these are precision + recall measurable); literature-only types (resistance, biomarker, safety) are precision-only.

The relation extraction strategy: 13 ontology-bound relation types, GLiREL zero-shot over abstracts, per-edge confidence carried through to downstream queries. The confidence propagation is what makes the structured output trustworthy for multi-hop traversals.

Compared against Claude Sonnet 4.6 + web search and GPT-4o + web search on 12 multi-hop pre-clinical questions. The interesting finding: LLMs with web search are more competitive on narrative recall than I expected. The structured-KG approach wins on structured output (ranked, confidence-scored, traceable to source documents).

Open artifacts:
 - Paper: https://zenodo.org/records/20557287
 - Data archive (Zenodo-DOI'd CSV): github.com/sourabhnk/mosaic-data
 - Reproducibility Docker: github.com/sourabhnk/mosaic-reproducibility
 - System: getmosaic.dev (free tier, MCP server access)

 Limitations: oncology-only for now; modality classification is heuristic SMILES (a learned classifier is on the roadmap); 320/363 targets have AlphaFold coverage.

 Genuinely interested in feedback from anyone applying GLiREL or similar zero-shot RE methods to biomedical text at scale, especially on the relation taxonomy design and ground-truth construction.

Hello all! My first post here. Working in the information/intelligence business for pharma. I recently built knowledge graph driven MCP tools for R&D intelligence. The architecture ingests targets, drug candidates, papers, patents, trials and several other data points to answer critical questions for researchers working in early R&D. Currently, it covers 300+ oncology targets and work is on for expanding coverage.
During evaluations with a small set of queries, the tool fared better than Claude with web search on at least 3 out of 10 questions.

I have documented the approach here: https://zenodo.org/records/20557287

and the tool itself is available for testing here: https://getmosaic.dev

Looking for feedback from early users in this community. Would love to hear any issues with the tool or any features that you would like to see.

Live now at https://tactical-app.work. Also available on Shopify App Store. Woo commerce plugin soon.

What it does:
ChatGPT, Perplexity, Claude and many other AI agents visit e-commerce stores at meaningful volume to research products before recommending them to your customers. They’re invisible to standard analytics because GA (Google) filters them out as bots. Tactical classifies every visitor session at the edge (UA + IP + behavioural heuristics), tells you which agent visited, what they looked at, and scores intent (browse/evaluate/high intent). Free Scout tier available at 100 agent sessions/week with no credit card.

Stack:
- React Router v7(TS) + Prisma + Neon Postgres
- Redis streams
- Railway for hosting

Hardest design decision: classifying at the edge vs at the dashboard. Edge gives you sub 50ms classification latency and lets you do per-page-load decisions (e.g. serve agents a different version of the product page in future). But it means the classifier logic ships in a Worker and has to stay in sync with the dashboard’s view. Went edge - happy with the tradeoff so far.

Most surprising bug: pinned pnpm to @latest in my Dockerfile, pnpm 11 dropped support for Node 20, broke every Railway deploy for two days while still falling back silently to the previous build. Fixed by pinning to pnpm@10.0.0 explicitly.

Demo: https://tactical-app.work

Shopify App Store: search ‘Tactical’

Disclosure: I’m the founder, building solo. AMA on the build, stack or agent-traffic landscape.

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