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GaiaLab AI — Technical Platform White Paper

Version 0.1.1 · May 2026 Platform: https://www.gailabai.com

Abstract

GaiaLab is an AI-powered biological intelligence platform that transforms gene panels into structured, citation-linked research insights in under 60 seconds. The platform aggregates live data from 75+ biological databases,¹ applies a six-agent AI debate framework for hypothesis generation and critique, and produces PMID-grounded therapeutic hypotheses ranked by a six-factor scoring model. An MCP (Model Context Protocol) server interface allows direct integration into AI assistant workflows. All outputs are computational research hypotheses requiring independent experimental validation.

1. Introduction

Translational genomics research faces a reproducibility and synthesis bottleneck: the information required to reason about a gene panel is distributed across dozens of databases, literature corpora, and clinical trial registries, none of which share a common schema. A researcher submitting a panel of 3–10 genes to understand their disease biology must manually cross-reference PubMed, ClinicalTrials.gov, ChEMBL, gnomAD, UniProt, AlphaFold, KEGG, Reactome, COSMIC, and dozens of other sources — a process that takes days and produces results that are difficult to cite, compare, or reproduce.

GaiaLab automates this synthesis layer. It does not replace experimental biology; it accelerates the hypothesis-generation and literature-triangulation steps that precede it. Every output carries PMID citations, confidence labels, and an explicit evidence-quality flag so researchers can distinguish AI-generated inference from database-backed assertion.

2. Architecture Overview

2.1 Runtime Stack

ComponentTechnology

RuntimeNode.js 22, ESM modules throughout DeploymentRailway (Nixpacks, auto-deploy from GitHub main) DatabasePostgreSQL (Railway / Neon) via pg AI providersDeepSeek → OpenAI → Google Gemini → Anthropic Claude (failover chain) MCP interface@modelcontextprotocol/sdk v1.24 Python inferencePython 3.11 in py/ for SAE and ESM-2 protein embeddings CachingL1 in-memory LRU (1 h TTL) + optional L2 disk tier 3D visualizationThree.js + 3d-force-graph

2.2 Request Lifecycle

`` User submits gene panel + disease context │ ▼ Rate / quota gate (tier-based: free / researcher / enterprise) │ ▼ Gene normalisation ── HGNC alias resolution, symbol canonicalisation │ ▼ Parallel data fetch ── 75+ sources via Promise.allSettled() [~15–40 s cold] │ ▼ Literature cache check ── 5-min in-memory keyed by genes + disease │ ▼ Evidence ledger build ── PMID validation, polarity classification, grounding attribution, knownPmid backfill │ ▼ Drug repurposing engine ── Six-factor scoring, tier assignment, AlphaFold structural bonus, DepMap essentiality │ ▼ Six-agent AI debate ── Parallel: Hypothesis · Critic · Evidence · Innovation · Risk · Synthesis agents │ ▼ Convergence scoring ── Cross-source family validation (PubMed, ClinicalTrials, FDA, ChEMBL, structural, network) │ ▼ Insight assembly + grounding gate ── PMID linkage, confidence labelling, pathway FDR │ ▼ Snapshot persistence + KG write ── PostgreSQL, shareable URL │ ▼ Structured JSON response ── Rendered in analyze.html widget `

Typical latency: 30–60 s cold start; < 1 s from L1 cache.

3. Data Layer

3.1 Source Coverage

GaiaLab fetches from 75+ biological databases¹ across seven domains. All clients follow the same defensive pattern: they never throw; they return partial results with an error field so Promise.allSettled() can continue regardless of upstream failures.

Gene annotation & variation AlphaFold EBI · ClinVar · Ensembl · gnomAD (variant + ancestry + constraint) · GWAS Catalog · HGNC · OMIM · UniProt · VEP · NCBI Gene · ClinGen · Monarch Initiative

Pathway & functional Enrichr · GO (Gene Ontology) · JASPAR (transcription factor binding) · KEGG · MSigDB · PathwayCommons · Reactome · ChEA3

Interaction & network BioGRID · IntAct · STRING · STRING-DB partners · ComplexPortal · SynLethDB (synthetic lethality)

Literature PubMed (NCBI Entrez) · Europe PMC · PubMed Central full-text (JATS XML → quantitative extraction: IC50, p-values, HR, OR, fold-changes) · bioRxiv · Semantic Scholar · OpenAlex · preprint monitor

Drug & clinical ChEMBL · CIViC · ClinicalTrials.gov API v2 · DGIDb · DrugBank · DrugCentral · FDA Regulatory · OncoKB · OpenFDA adverse events · OpenTargets (genetics + disease–gene association) · OpenTargets Genetics · PubChem (compound + bioassay) · RxNorm DDI · TTD · PharmGKB · HMDB (metabolomics)

Omics & cancer CBIOPORTAL · COSMIC Signatures · CPTAC (proteomics) · DepMap (cancer dependency + co-essentiality) · GDSC (drug sensitivity) · GTeX (expression + eQTL) · HPA (protein atlas) · MetaboLights · PRIDE (proteomics) · ProteomicsDB · scRNA (Cell × Gene) · TCGA (mutation + survival) · AGR (Alliance of Genome Resources)

Structural AlphaFold (pLDDT → druggability score) · PDB (experimental structures) · NGL molecular viewer

Regulatory / patent Drug resistance intelligence · FDA regulatory intelligence · Patent status + expiry · Regulatory intelligence client · LINCS (perturbation signatures)

3.2 Aggregator Architecture

Sources are grouped into seven aggregators (src/data/aggregators/):

- Gene aggregator — symbol resolution, aliases, disease associations

Context penalty: Off-label drugs with contextRelevance < 20 receive a ×0.3 score multiplier; those with contextRelevance 20–34 receive ×0.45. These are mutually exclusive — only the most severe penalty applies.

4.4 Convergence Scoring

Each drug is evaluated against six independent evidence families. A drug scoring 4/6 or higher is considered "convergent" — supported by multiple orthogonal source types rather than a single strong signal:

FamilyPasses when

pubmed≥ 1 PMID linked to drug–disease combination clinicaltrials≥ 1 trial record in ClinicalTrials.gov fdaFDA approved, phase ≥ 3, OR phase label matches "approved/phase 3/4" chemblConfirmed binding targets or bioactivity records exist structuralAlphaFold pLDDT ≥ 50, PDB structures present, or hasAlphaFold=true network≥ 3 interaction neighbours or hasNetworkProximity=true

5. Evidence Grounding System

5.1 The Grounding Problem

AI-generated insights are only as credible as their citations. GaiaLab's grounding pipeline ensures that every strategy card and pathway insight either carries validated PMIDs or is explicitly labelled as a hypothesis.

5.2 PMID Attribution Pipeline

Attribution occurs in two passes:

Pass 1 — _preAttributePmids: After data fetch, each insight item is matched against the full literature pool. Matching uses token overlap between the item's text (label + mechanism + gene symbols) and each paper's title + abstract. A gene-symbol match requires only 1 overlapping token; non-gene content requires 2. Up to 2 PMIDs are collected per item (collecting two is important — items with ≥ 2 PMIDs plus a disease/gene text match reach grounded status, the highest evidence tier).

Pass 2 — buildInsightLinks: Inside the polarity assignment loop, secondary attribution runs the same gene-aware 1-token / 2-token rule against the known-PMID set, collecting up to 2 additional papers.

5.3 Evidence Status Classification

Each insight item receives an evidenceStatus:

StatusCriteriaGrounding weight

grounded≥ 1 PMID + supporting polarity, OR ≥ 2 PMIDs + disease/gene text match1.0 supported1 PMID + disease/gene text match0.6 hypothesis0 PMIDs0.0

The grounded ratio — the proportion of pathway + strategy items reaching grounded or supported — is reported on every analysis and monitored by the canary suite. Observed range: 28% (cold start, PubMed rate-limited) to 70%+ (warm cache, full paper pool).

5.4 Polarity Classification

Each linked PMID is classified as support, neutral, or contradict relative to the insight claim. Items in the knownPmids set with neutral polarity are promoted to support (papers confirmed as relevant by prior analysis are treated as supporting unless explicitly classified as contradictions).

6. Multi-Agent Reasoning Framework

6.1 Six-Agent Debate

GaiaLab uses a structured six-agent debate (enabled by default via GAIALAB_MULTI_AGENT_ENABLED=true) where each agent has a distinct epistemic role:

AgentRole

HypothesisAgentGenerates primary mechanistic hypotheses from pathway and gene evidence CriticAgentChallenges hypothesis validity; flags unsupported claims, contradictions EvidenceAgentGrounds claims in database facts (ChEMBL bioactivity, OpenTargets scores, clinical trial outcomes) InnovationAgentIdentifies non-obvious therapeutic angles; queries active recruiting trials RiskAgentEvaluates adverse event burden, drug–drug interactions, safety flags SynthesisAgentIntegrates the five prior perspectives into a consensus output

Each agent operates independently in parallel. The SynthesisAgent receives all five outputs and produces the final structured response.

6.2 Live Database Enrichment per Agent

Before the debate, src/ai/agent-data-retrieval.js pre-fetches four live APIs and formats the results per agent:

- OpenTargets disease–gene associations → EvidenceAgent + CriticAgent

Every completed analysis writes to a PostgreSQL knowledge graph (kg_nodes + kg_edges + kg_cooccurrence tables). Nodes represent genes, drugs, pathways, and disease contexts. Edges represent:

- Drug → target (gene) bindings

Board data is stored in PostgreSQL (disease_boards, board_evidence_items, board_alerts tables). Case study pages for selected boards (lecanemab/AD, adagrasib/NSCLC, adagrasib/PDAC) provide focused mechanistic analysis with open research questions.

9. MCP Server Interface

GaiaLab exposes a Model Context Protocol server at POST /mcp, allowing AI assistants (Claude Desktop, custom agents built with the Anthropic Agent SDK) to call the platform as a tool.

Tool: gaialab_generate_insights

Input schema (Zod-validated): `json { "genes": ["string"], "diseaseContext": "string", "audience": "researcher | clinician | general" } `

Each POST creates a fresh McpServer + StreamableHTTPServerTransport instance. Responses carry Access-Control-Allow-Origin: * for cross-origin use. The MCP interface is the primary integration surface for research workflow automation.

10. Workspace & Collaboration

Authenticated users (Stripe-backed tiers) receive persistent workspaces with:

- Saved analyses — shareable snapshot URLs, reproducible replay

When GAIALAB_INTERPRETABILITY_ENABLED=1, GaiaLab spawns Python 3.11 subprocesses from py/ to run sparse autoencoder (SAE) inference over ESM-2 protein language model embeddings. This surfaces learned biological features from the ESM-2 representation that are not explicitly encoded in database annotations — an experimental interpretability layer for protein function.

Requirements: PostgreSQL, Python 3.11, py/requirements.txt, ESM-2 model cache. This feature is disabled by default on the public deployment.

12. Evaluation & Benchmarking

12.1 AUROC (Retrospective)

A retrospective AUROC of 0.545 (95% CI bootstrap: 0.526–0.562) was computed in March 2026 against 529 predictions across 22 disease areas, using ClinicalTrials.gov completed trial matches as the gold standard. The random baseline is 0.50. This represents a modest but consistent signal above random. This is not a clinically validated predictor.

12.2 Temporal Holdout

A separate temporal holdout benchmark using 22 known drug approvals (held out by year) showed AUROC 0.90 with 8/8 negative controls correctly rejected. This benchmark is on a smaller, curated dataset and should be interpreted accordingly.

12.3 Grounding Rate

The primary quality signal for day-to-day health is the grounding ratio — fraction of insight items with at least one validated PMID. The canary suite monitors this on every push. The npm run verify:engineering gate includes a grounding check (test:grounding-gate).

12.4 Continuous Evaluation

The evaluation suite (scripts/gaialab-eval.js) supports:

The gate is enforced by a canary runner (scripts/canary.js) that executes a full live analysis against https://www.gailabai.com and validates: completion time, drug candidate count, grounded ratio, job failure rate, and trust page availability.

14. Security Model

- Secret detection: scripts/check_secrets.js runs as a git pre-commit hook and in every CI job. Matches known patterns for API keys, connection strings, and credentials.

Optional premium data source keys: BIOGRID_API_KEY, DISGENET_API_KEY, DRUGBANK_API_KEY, SEMANTIC_SCHOLAR_API_KEY`.

16. Limitations & Responsible Use

GaiaLab is a computational research tool. Its outputs are hypotheses, not clinical recommendations. Specific limitations:

- All therapeutic suggestions require independent experimental validation before any clinical application.