Resources
Guides, insights, and documentation
Everything you need to get the most from Amass — and to stay informed on life science research intelligence.
What the Building an AI Scientist London Hackathon taught us about Tech x Bio
What we learned from 50 builders and what it taught us about building successfully in Tech x Bio.
How to do scientific work in Claude: from general AI to domain expert
A live walkthrough of connecting Claude to the Amass scientific data layer, then taking a real biologics asset evaluation end to end with every answer cited.
Killing drug targets early: how Apoptosis AI won the London hackathon
Two winning teams from the London "Building an AI Scientist" hackathon built on the Amass Cores, and both were still building on them more than a month later. Here is what they built and why they reached for a curated, linked data instead of a search tool.
Build with Lluís — BD & Competitive Intelligence Tools, From Idea to Internal App
One real BD question, turned into a working internal tool live — using Claude connected to the Amass API, then Lovable for the app. Every number traceable to its source record.
DrugCore + RegulatoryCore — Live Demo
A live look at DrugCore and RegulatoryCore — linking a drug to its approvals, agency reviews and pivotal trials in one place.
From molecule to market: introducing DrugCore and RegulatoryCore
Introducing DrugCore and RegulatoryCore: drugs and FDA/EMA approvals, harmonized and linked to the trials and literature you already query.
How to Use the Amass MCP in Your Scientific Work — Live Demo
A live walkthrough of the Amass MCP inside Claude, ChatGPT and Cursor — how to connect it, three real research use cases, and Q&A.
Introducing GeneCore: One Link From a Drug to Its Target Biology
Introducing GeneCore, one canonical record per gene, plus mechanism of action on every DrugCore record. See how the two connect a drug to its target biology in a single API link.
Eval First, Model Second: Inside the TrialCore Iteration Loop
A look at the evaluation infrastructure behind one of Amass's core retrieval services. How we build the datasets, why we trust the labels, and the loop we run to actually move metrics.
Comparing 4 biomedical MCPs using a fixed agent harness
Aggregate results and a worked NSCLC case study from a four-server biomedical MCP benchmark (Amass, PubMed, Consensus, Paperclip).
Why biomedical AI fails at retrieval before it fails at reasoning
A 10-question benchmark across Amass BioMedCore, Claude with PubMed, and ChatGPT with web search. Retrieval, not reasoning, is biomedical AI's bottleneck.
Monitor description and examples
Introducing Planner: A New Way to Guide Your Research
Introducing *Core: The Intelligence Architecture Behind Amass
All six external data sources are being renamed to a unified *Core naming convention — BioMedCore, ScholarCore, PatentCore, RegulatoryCore, TrialCore, and…
Global Data Context and Placeholder Positioning
Enhanced accessibility and configurability of report placeholders and global data context in the Amass platform improve user experience.
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