API
Build on the life-science data engine.
One REST API across six live cores — biomedical literature, clinical trials, drugs, genes, patents, and FDA + EMA regulatory — cross-linked and cited to source.
Go from a gene to the drugs that target it, the trials that study it, the papers behind it, and the regulatory decisions that cover it — in one call. Structured JSON, typed schemas, daily updates.
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Join nowBuilders
What people build with it
Founders, fellows, and hackathon teams who pointed their agents at the API and shipped.
We'd tried scraping patent data ourselves and it was a nightmare. With PatentCore we built a full freedom-to-operate landscape of our space in a couple of days.
The tool is really great — I've used it personally for my structural-biology workflows. BiomedCore surfaced epitope annotations we couldn't find in structured databases like UniProt.
Of everything we built on during the hackathon, Amass was the most valuable API. I built a landscape around patents and trials — very, very valuable for understanding where the field is going.
Data
Six live cores. More on the way.
Query curated life-science databases directly — no wrappers, no hallucinations. Every record carries full provenance and typed metadata, and cross-links to the others, so a gene, paper, trial, drug, patent, and approval all resolve to the same entity.
BiomedCore
/cores/biomedcore/records
Peer-reviewed biomedical literature from PubMed and PubMed Central. Every record includes MeSH terms and IDs, publication types (RCT, meta-analysis, systematic review), JuFo 0–3 journal quality tier, citation count, DOI, PMID, and PMCID. Optional: full text for PMC articles, ORCID-disambiguated authors with ROR-mapped affiliations, and links to the trials each paper describes in TrialCore.
Filter parameters
TrialCore
/cores/trialcore/records
Clinical trial records from ClinicalTrials.gov and international registries. Includes protocol data, eligibility criteria, primary and secondary endpoints, sponsor details, arm groups, recruitment status, NCT ID, and the full lifecycle — start, completion, results posting, and why stopped. Outcome measurements are available as structured values per arm, not PDF blobs. Records link back to the publications that describe them in BiomedCore.
Filter parameters
DrugCore
/cores/drugcore/records
ChEMBL-derived drugs and molecules, harmonized. Names, trade names, and synonyms; chemical structures with InChIKey and SMILES; modality classification; and highest clinical stage from preclinical through approval. Each drug cross-links to the trials, papers, and regulatory authorizations it appears in.
Filter parameters
RegulatoryCore
/cores/regulatorycore/records
FDA and EMA regulatory data on one unified schema. FDA application records, drug labels, review documents, and approval letters; EMA EPARs, assessment reports, and SmPCs. Each record carries a unified authorization status, designations (Breakthrough Therapy, PRIME, Accelerated Approval), and orphan status — and cross-links to the drug it covers in DrugCore.
Filter parameters
GeneCore
/cores/genecore/records
43,000+ harmonized human gene records from HGNC, NCBI, UniProt, and Open Targets. Druggability and tractability assessments, target safety data, genetic constraint via gnomAD v4.0 (pLI, LOEUF), and cellular essentiality from DepMap CRISPR screens. Each gene cross-links to the drugs that target it in DrugCore, the trials that study it in TrialCore, and the papers that cite it in BiomedCore — from gene to clinical evidence in one call.
Filter parameters
PatentCore
Preview/cores/patentcore/records
Patent publications with English-preferred full text — title, abstract, claims, and description — plus bibliographic metadata (publication and application numbers, jurisdiction, kind code, family id), CPC/IPC classifications, inventors and assignees, four separate dates, and citation lineage. Search returns one publication per family, the most relevant member, and every record cross-links to the drugs it mentions in DrugCore and the papers it cites in BiomedCore.
Filter parameters
How It Works
From zero to your first response — three steps
Create an account and get an API key
Sign up at platform.amass.tech, go to API Keys, and click Create API Key. Your key starts with amass_ and is shown only once — copy it immediately. Generate separate keys for dev and prod; both share your plan quota.
production
amass_live_••••••••••••
development
amass_dev_••••••••••••
Make your first search request
Pass your key in the Authorization header on every request. Every core answers the same three-endpoint pattern — search, get by Amass ID, batch lookup — so one query shape works across all six. Returns up to 300 records per call.
$ curl \
"https://api.amass.tech/api/v1/
cores/biomedcore/records?
query=alzheimer+tau
&minJournalQualityJufo=2
&limit=10" \
-H "Authorization: Bearer amass_YOUR_KEY"Build your workflow — Python, JavaScript, or any HTTP client
No official SDK needed — the API is plain REST. Use requests in Python or fetch in JavaScript. Handle 429s with exponential backoff; the rate-limit window resets every 60 seconds.
import requests
BASE = "https://api.amass.tech/api/v1"
HEADERS = {"Authorization": "Bearer amass_YOUR_KEY"}
# Search BiomedCore
resp = requests.get(
f"{BASE}/cores/biomedcore/records",
headers=HEADERS,
params={
"query": "GLP-1 NASH liver fibrosis",
"minJournalQualityJufo": 2,
"limit": 50,
},
)
records = resp.json()["data"]["records"]
# → list of { amassId, pmid, doi, title,
# citationCount, journalQualityJufo, … }Use Cases
What teams build with the API
Real patterns used by R&D teams, data engineers, and builders. Every snippet below is copy-paste ready.
Literature mining pipeline
Pull high-quality papers on any target, pipe the structured output into pandas, and sort by citation count for a prioritised reading list — all in 10 lines.
import requests, pandas as pd
r = requests.get(
"…/cores/biomedcore/records",
headers={"Authorization": "Bearer amass_…" },
params={
"query": "GLP-1 receptor agonist NASH",
"minJournalQualityJufo": 2,
"minPublicationDate": "2023-01-01",
"limit": 300},
)
df = pd.DataFrame(r.json()["data"]["records" ])
df.sort_values("citationCount").head(20)No-code trial tracker — built in one afternoon
Paste your Amass API key into Lovable and describe what you want. It wires up the TrialCore endpoint, builds a filterable table, and deploys — no engineering required.
“Build a dashboard that fetches all recruiting Phase 3 NASH trials from the Amass API. Base URL: https://api.amass.tech/api/v1. Auth header: Authorization: Bearer amass_xxx. Show a filterable table: trial name, NCT ID, sponsor, enrollment, start date.”
GET /cores/trialcore/records
?query=NASH
&phase=PHASE3
&overallStatus=RECRUITING
&limit=100Nightly trial monitoring alerts
An n8n HTTP Request node polls TrialCore every night and pushes a Slack message when new recruiting trials appear for your indication. Zero code.
Schedule
every night
HTTP GET
TrialCore
IF
new records?
Slack
notify team
Method: GET
URL: …/cores/trialcore/records
Auth: Bearer amass_YOUR_KEY
# Query params
query: obesity GLP-1
phase: PHASE3
overallStatus: RECRUITINGInternal research app in 20 lines
A Streamlit app that wraps BiomedCore into an internal search tool — live search with JuFo score and citation count visible at a glance. Deployed on Streamlit Community Cloud for free.
import streamlit as st, requests
q = st.text_input("Search papers")
if q:
r = requests.get(
"…/cores/biomedcore/records",
headers={"Authorization": f"Bearer {st.secrets['KEY']}" },
params={"query": q, "limit": 20, "minJournalQualityJufo": 2 },
)
for rec in r.json()["data"]["records"]:
st.markdown(f"**{rec['title']}** \n{rec['doi']}")
st.caption(f"JuFo {rec['journalQualityJufo']} · {rec['citationCount']} citations")Developer Resources
Everything you need to get to production
API Overview
Base URL, authentication, rate limits, error codes, and response envelope.
See moreBiomedCore Reference
Full parameter and field reference for all three BiomedCore endpoints.
See moreTrialCore Reference
Full parameter and field reference for all three TrialCore endpoints.
See moreDrugCore Reference
22K+ harmonized drugs and molecules — modality, clinical stage, structure, and mechanisms of action.
See moreRegulatoryCore Reference
FDA and EMA authorizations on one schema, plus full-text search across labels, SmPCs, reviews, and EPARs.
See moreGeneCore Reference
43K+ human genes — druggability, genetic constraint, and cross-links to DrugCore and TrialCore.
See morePatentCore Reference
16M+ patent publications in preview — full-text search, family collapsing, and cross-links to drugs and papers.
See moreAPI Roadmap & Changelog
What shipped, month by month, and what's being built next for the public REST API.
See moreQuickstart Guide
From zero to your first cited response in under five minutes.
See moreLLM Quick Reference
Self-contained reference designed for AI agents and automated tools.
See moreOpenAPI Spec
Machine-readable spec for code generation, Postman imports, and type inference.
See moreDeveloper Community
Join the Amass developer community — talk to other life-science AI developers and builders.
Join nowShip faster with cited science
Start building with the Amass API today. Enterprise plans available for teams that need dedicated support.
By using the API you agree to the API Terms of Service.