Give Drug Discovery Teams AI That Carries Research Across Every Session
A drug mechanism you investigated last month should inform the clinical trial query you're running today. MemoryLake connects pharmaceutical research across sessions, surfaces conflicts in safety data automatically, and gives your AI access to 40M+ academic papers and 2M+ drug records without manual uploads.
Give Drug Discovery Teams AI That Carries Research Across Every Session
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The Memory Problem
Pharmaceutical research is cumulative by nature — each finding builds on prior work. But AI sessions aren't cumulative. Every time a researcher opens a new chat, they're pasting in the same compound background, re-explaining the mechanism hypothesis, and rebuilding the regulatory context that was established three sessions ago. When safety data from two sources contradicts, there's no automatic flag — just a silent overwrite.
What MemoryLake Does Differently
Built-in access to 40M+ papers and 2M+ drug records
MemoryLake includes PubMed, arXiv, and bioRxiv academic literature, 500K+ clinical trials, and 2M+ FDA/DrugBank drug records as native datasets. Query them directly from your AI session — no upload, no preprocessing step.
Conflict detection for contradictory safety data
When two sources give different findings on drug interactions, toxicity, or mechanism, MemoryLake's Fact Memory flags the conflict with attribution to both. Contradictions surface before they influence downstream analysis.
Research continuity across the full study lifecycle
Event Memory tracks clinical milestones, regulatory submissions, and trial phase transitions in order. Conversation Memory stores every AI research session permanently. Pick up any project exactly where it was left, regardless of which team member ran the last session.
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How It Works
- Connect — Access built-in pharmaceutical datasets directly, or bring in internal research via REST API, Python SDK, or document upload. MemoryLake's D1 Engine parses complex PDF research papers and clinical documents automatically.
- Structure — Drug facts, regulatory findings, trial events, and session research are stored in the appropriate memory type with full provenance tracking. Every stored fact carries its source.
- Reuse — Any supported AI model — Claude, ChatGPT, Gemini, or your internal model endpoint — draws from the same persistent research memory. Switch models for different tasks without rebuilding context.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Continuing a compound investigation | Re-paste prior research into every session | Full research history loads automatically |
| Contradictory safety findings | May go undetected until review stage | Conflict detection flags discrepancies with sources |
| Literature review | Manual search and upload of papers | 40M+ papers queryable directly in-session |
| Cross-researcher continuity | Context lives in one researcher's session | Shared memory accessible across the team |
Built For
MemoryLake is built for pharmaceutical research teams and drug discovery scientists who work across long research cycles involving literature review, compound analysis, clinical trial tracking, and regulatory preparation. It is equally useful for clinical scientists managing ongoing trial data and regulatory affairs teams who need persistent, auditable records of safety findings and submission history.
Try this use case on your own account
A drug developer keeps its Phase II trial protocol in shared memory: six short pages on dosing, prohibited medicines, liver safety, eligibility, visit windows and SAE reporting. A trial assistant answers the study team from them through MemoryRouter. Asked for a rule, it is right. Asked to apply that rule to a participant ("93 kg this morning, what dose today?"), it often is not, even with the right page pasted into its prompt: "180 mg" past a 160 mg cap, "Yes" for a 76-year-old when the limit is 75, "No record" for an ALT above the hold line. Retrieval is not the problem. Asking for the rule word for word, then one step per line, fixes most of it, and the errors that remain sit in a line a person can check.
What you will see
- Six protocol pages imported into MemoryLake;
search --types document --top-k 1finds the page with the rule for all 13 questions the protocol covers, and the app pastes that page into the prompt - Fifteen questions (six rules, seven applied to a participant, two the protocol does not cover), 3 answers each with gpt-4o-mini, over 8 runs: with the right page in the prompt, the rules themselves 144 of 144 right, the same rules applied 90 of 168
- Quote the rule, then work it: applied questions 131 of 168; every quoted rule was on the page word for word (168 of 168), and every remaining wrong answer shows its wrong step ("Day 32 (6 November 2026)")
- Plus
router chat --boundaryas the first setup, anasksubcommand that works one question step by step, and a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/rule-application-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
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Frequently asked questions
How current is the built-in pharmaceutical data?
How current is the built-in pharmaceutical data?
MemoryLake's built-in datasets include 40M+ academic papers from PubMed, arXiv, and bioRxiv; 500K+ clinical trials; and 2M+ FDA/DrugBank drug records. These are maintained datasets, and you can supplement them with your own internal research data at any time.
How does memory provenance work for regulatory purposes?
How does memory provenance work for regulatory purposes?
Every fact stored in MemoryLake carries full provenance — the source document, the session that created it, and a complete audit trail of any modifications. Git-like versioning means you can review the history of any stored finding, which is particularly relevant for regulatory documentation requirements.
Is sensitive compound and trial data secure?
Is sensitive compound and trial data secure?
Yes. All data is encrypted with AES-256 end-to-end. MemoryLake is ISO 27001 certified, SOC 2 Type II compliant, and meets GDPR and CCPA requirements. Internal research data stays within your organization's account and is not used externally.