Every molecule, already understood

Toxicity. Drug-likeness. Regulatory status. CYP450 interactions. We pre-computed it all so your team doesn't have to. Ask a question, get an answer—not a compute job.

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122M+

Enriched compounds

Properties per molecule

<50ms

Lookup latency

Regulatory databases

The work is already done

Most molecular databases hand you raw structures and leave the hard part to you. Is this compound toxic? Controlled? Likely to fail in trials? Getting those answers means running models, querying regulators, searching literature—hours or days per compound.

We ran the models once, for all of them. And every answer comes with FAVES compliance baked in—eight regulatory jurisdictions screened inline, not bolted on after the fact. The question you're about to ask already has an answer waiting.

What changes for your team

The work that used to take days now takes seconds. Here's what that looks like in practice.

Compound assessment in seconds, not hours

Every molecule in our index already has its ADMET profile, drug-likeness scores, and regulatory status computed. Your team asks a question and gets an answer—no model queue, no compute allocation, no waiting.

Compliance inline, not at the gate

FAVES screens every prediction against eight regulatory jurisdictions—DEA, FDA, EPA, CWC, EU REACH—as it's returned, not after. Compliance runs as a compute layer inside the pipeline, not a manual checkpoint at the end.

Research scattered across five sources, unified

Curated papers, patents, preprints, clinical trials, and ChEMBL bioactivity data—searched together. One query gives you the complete landscape instead of five separate searches.

Lead optimization with guardrails

Generate drug-like variants and know immediately whether they're viable. Every candidate is auto-screened through FAVES for regulatory compliance and structural alerts before it reaches your team.

No new tools to learn

Works inside any MCP-compatible AI assistant—Claude, ChatGPT, Cursor, Windsurf, GitHub Copilot, and more. Natural language in, molecular intelligence out. No dashboards, no SDKs, no context switching.

Your warehouse, enriched

Pull compounds from Snowflake or Databricks, run ADMET and compliance processing, push enriched results back. Your existing data infrastructure gains molecular intelligence without new pipelines.

Ask naturally, get intelligence

No query language. No dashboard. Just describe what you need.

Lead Optimization

"Optimize this lead for better oral bioavailability while maintaining selectivity."

Generate variants, predict ADMET, screen for safety—all in one response.

Safety Assessment

"Is this molecule safe to develop? Check for regulatory concerns."

Instant screening against DEA, FDA, EPA, CWC, and structural alerts.

Literature Review

"What's known about CDK4/6 inhibitors in breast cancer?"

Search papers, patents, preprints, and clinical trials in one query.

Virtual Screening

"Find molecules similar to palbociclib with QED > 0.6 and no PAINS."

Filter 122M compounds by similarity, properties, and compliance.

Structure Prediction

"Predict the structure of this kinase domain."

State-of-the-art protein folding with confidence scores and 3D visualization.

Bidirectional Pipeline

"Pull imatinib from my Databricks table. Run ADMET and FAVES. Push enriched results back."

Pull from your warehouse, enrich with AI, push back. One prompt.

How it works

Connect once

One URL, one API key. Works with any MCP-compatible AI assistant—Claude, ChatGPT, Cursor, Windsurf, GitHub Copilot, and more. Setup takes under a minute. See setup guide →

Ask naturally

Describe what you need in plain language. Your AI recognizes molecular queries and selects the right tools automatically—no syntax, no parameters, no dashboard.

Get answers with compliance

Every response includes automatic safety screening across eight regulatory jurisdictions. Lead optimization generates only compliant variants. Nothing slips through unchecked.

Start in under a minute

One URL. One API key. Complete molecular intelligence in your next conversation.

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