🔬 Eldric Science Worker

AI-powered scientific computation engine combining bioinformatics, CRISPR design, molecular docking, and laboratory management with multi-backend LLM integration for intelligent research assistance.

eldric-scienced v4.0.0 · Port 8897 🤖 AI-Assisted Research

🚀 New: AI-Assisted Research

The Science Worker now integrates with any LLM backend (Ollama, vLLM, OpenAI, Anthropic) for AI-assisted analysis. Generate hypotheses, interpret results, design experiments, and delegate complex research tasks to the Agent Worker's multi-agent system.

What Can You Do?

🧬

Analyze Sequences

Upload DNA, RNA, or protein sequences for comprehensive analysis including GC content, molecular weight, translation, and BLAST search.

✂️

Design CRISPR Guides

Generate optimized guide RNAs for Cas9/Cas12a with off-target prediction, efficiency scoring, and base/prime editing support.

💊

Discover Drugs

Perform molecular docking, predict ADMET properties, and analyze drug-target interactions with GPU acceleration.

🏢

Manage Lab Data

Track samples, experiments, and protocols with full regulatory compliance (FDA 21 CFR Part 11, GLP, GMP).

🤖

AI Analysis

Use LLMs to interpret results, generate hypotheses, suggest experiments, and write research summaries automatically.

🔗

Access 62 APIs

Query NASA, CERN, NCBI, UniProt, and 62 scientific databases through the integrated Data Worker connector.

Architecture

Science Worker - AI-Integrated Research Platform
Data Worker Port 8892 • Scientific APIs • Databases • RAG Storage NASA, NCBI UniProt, PDB Science Worker Port 8897 · AI-Powered Computation 🧬 Bioinformatics BLAST, Align, Variants ✂️ CRISPR Guide Design, Off-target 💊 Drug Discovery Docking, ADMET 🏢 LIMS Samples, Compliance 🤖 AI-Assisted Research Analysis • Hypotheses • Experiments Multi-Backend: Ollama | vLLM | OpenAI | Anthropic Inference Worker Port 8890 • LLM Completion • Chat / Analysis • Embeddings Direct LLM Agent Worker Port 8893 • Multi-Agent Tasks • Literature Review • Protocol Generation • Agentic RAG Complex Tasks Controller :8880 Cluster • Licensing • Discovery LEGEND Data Flow AI Request

🔬 Research Agent

The Science Worker operates as an intelligent Research Agent that orchestrates federated searches across multiple scientific data providers. Rather than making direct API calls, it delegates to the Data Worker's 62 scientific connectors and synthesizes results into comprehensive research reports with citations.

🌐 Federated Search Architecture

When you conduct research through the Science Worker, it automatically queries multiple providers simultaneously (arXiv, Crossref, OpenAlex, PubMed, Semantic Scholar, etc.), normalizes the heterogeneous responses, extracts citations, and synthesizes findings into a unified research report. The Data Worker handles rate limiting, caching, and API authentication for all 62 providers.

Federated Research Pipeline
Research Query "machine learning" topic: "ai" Science Worker Port 8897 executeFederatedSearch() synthesizeResearch() delegate Data Worker Port 8892 arXiv Crossref OpenAlex PubMed S2 + 57 more Query Templates Rate Limiting Research Report summary: "Found 10 sources..." citations: [{title, authors, date, DOI, snippet}...] key_findings: [...] sources_consulted: 5

Research Agent Endpoints

POST /api/v1/research/search

Federated search across multiple scientific providers

POST /api/v1/research/conduct

Full research workflow: search → synthesize → report

POST /api/v1/research/plan

Generate a research plan with suggested providers

GET /api/v1/research/providers

List available providers grouped by topic

Example: Conduct Research

curl -X POST http://localhost:8897/api/v1/research/conduct \ -H "Content-Type: application/json" \ -d '{ "query": "machine learning", "topic": "ai", "providers": ["arxiv", "crossref", "openalex", "pubmed", "semanticscholar"], "max_results_per_provider": 5 }' # Response: { "query": "machine learning", "summary": "Found 10 sources across 4 providers. AI synthesis unavailable...", "citations": [ { "title": "Scikit-learn: Machine Learning in Python", "authors": "Pedregosa et al.", "date": "2011", "id": "10.5555/1953048.2078195", "provider": "crossref", "snippet": "Scikit-learn is a Python module integrating..." }, ... ], "key_findings": ["Machine learning with sklearn (crossref)", ...], "sources_consulted": 5, "sources_cited": 10, "generated_at": "2026-02-01T15:13:56Z" }

Supported Research Topics

📚 Literature

arXiv, Crossref, OpenAlex, PubMed, Semantic Scholar, Europe PMC

🧬 Life Sciences

NCBI, UniProt, Ensembl, PDB, ChEMBL, MyGene, ClinicalTrials

🌍 Earth Sciences

NOAA, USGS, Copernicus, GBIF, OBIS, iNaturalist, eBird

⚛️ Physics

CERN, GWOSC, INSPIRE-HEP, NASA, ESA, MAST

🔬 Chemistry

PubChem, COD, Materials Project, NIST

💊 Pharma

OpenFDA, DrugBank, ChEMBL, ClinicalTrials

🤖 AI-Assisted Research

The Science Worker integrates with multiple LLM backends to provide intelligent research assistance. Use AI for basic tasks directly, or delegate complex multi-step research to the Agent Worker.

AI Research Workflow
📊 DATA BLAST Results Docking Scores 🤖 ANALYZE Summarize Interpret 💡 HYPOTHESIZE Generate Ideas Rank Confidence 🧪 DESIGN Suggest Experiments Generate Protocols 📝 REPORT Literature Review Multi-Agent Analysis
💬

Direct LLM Access

For quick AI tasks, query LLMs directly through any configured inference backend.

  • Text completion for research summaries
  • Multi-turn chat for interactive analysis
  • Data interpretation and summarization
  • Hypothesis generation with confidence scores
  • CRISPR guide evaluation with AI scoring
  • Drug target prediction assistance
  • Experiment suggestion with protocols
Supported Backends
Ollama
vLLM
OpenAI
Anthropic
TGI
llama.cpp
🔗

Agent Worker Delegation

For complex multi-step tasks, delegate to the Agent Worker's multi-agent system.

  • Complex research task delegation
  • Automated literature reviews (up to 20+ papers)
  • Multi-agent collaboration (Researcher + Validator)
  • Protocol generation with constraints
  • Agentic RAG with iterative retrieval
  • Query decomposition for complex questions
  • Knowledge base integration
Agent Types
Researcher
Validator
Planner
Coder

AI API Endpoints

GET /api/v1/ai/status

Check LLM and Agent Worker availability

POST /api/v1/ai/completion

Generate text completion for research tasks

POST /api/v1/ai/chat

Multi-turn chat for interactive research

POST /api/v1/ai/analyze

AI analysis: summarize, interpret, suggest, compare

POST /api/v1/ai/hypothesis

Generate research hypotheses with confidence scores

POST /api/v1/ai/experiments

Get experiment suggestions with protocols

POST /api/v1/agent/research

Delegate complex research to Agent Worker

POST /api/v1/agent/literature

Request automated literature review

POST /api/v1/agent/multi-analysis

Multi-agent collaborative analysis

POST /api/v1/agent/protocol

Generate experimental protocols

Computational Features

🧬

Bioinformatics Engine

DNA/RNA/protein sequence analysis with built-in algorithms for molecular biology research.

  • Sequence analysis (GC content, molecular weight, melting temp)
  • DNA translation (6-frame, codon optimization)
  • BLAST alignment (local database or NCBI)
  • Multiple sequence alignment (Clustal, MUSCLE)
  • Variant calling and annotation (VCF)
  • Phylogenetic tree construction
Integrated Tools

BLAST+, HMMER, Clustal Omega, MUSCLE, SAMtools, BCFtools

✂️

CRISPR Design

Guide RNA design with off-target analysis and efficiency prediction for genome editing.

  • Cas9/Cas12a guide RNA design
  • Off-target site prediction (CFD/MIT scoring)
  • On-target efficiency prediction (Rule Set 2)
  • Base editing guide design (ABE/CBE)
  • Prime editing (pegRNA design)
  • HDR template generation
Algorithms

Doench scoring, CFD scoring, Lindel prediction, DeepCRISPR

💊

Drug Discovery

Molecular docking, property prediction, and structure analysis for pharmaceutical research.

  • Molecular docking (AutoDock Vina, GNINA)
  • ADMET property prediction
  • Lipinski Rule of Five / Veber rules
  • SMILES/InChI parsing and validation
  • Molecular fingerprints (ECFP, MACCS)
  • AlphaFold structure prediction integration
Integrated Tools GPU

AutoDock Vina, GNINA, RDKit, Open Babel, ESMFold

🏢

LIMS (Laboratory Management)

Laboratory Information Management with regulatory compliance for research environments.

  • Sample registration and barcode tracking
  • Experiment workflow management
  • Equipment calibration tracking
  • Reagent inventory management
  • Audit trails (21 CFR Part 11 compliant)
  • GLP/GMP compliance modes
Compliance

FDA 21 CFR Part 11, EU Annex 11, ISO 17025, GLP, GMP

REST API

Bioinformatics

POST /api/v1/bio/analyze

Analyze DNA/RNA/protein sequence (GC%, MW, Tm)

POST /api/v1/bio/translate

Translate DNA to protein (all reading frames)

POST /api/v1/bio/blast

BLAST search against local or NCBI databases

POST /api/v1/bio/align

Multiple sequence alignment

POST /api/v1/bio/variants

Variant calling from alignment

CRISPR

POST /api/v1/crispr/design

Design guide RNAs for target sequence

POST /api/v1/crispr/offtargets

Predict off-target sites for guide

POST /api/v1/crispr/base-edit

Design base editing guides (ABE/CBE)

POST /api/v1/crispr/prime-edit

Design prime editing guides (pegRNA)

Drug Discovery

POST /api/v1/pharma/compound

Parse SMILES, calculate molecular properties

POST /api/v1/pharma/dock

Molecular docking (Vina/GNINA)

POST /api/v1/pharma/admet

ADMET property prediction

POST /api/v1/pharma/structure

Fetch/predict protein structure

LIMS

POST /api/v1/lims/samples

Register new sample

GET /api/v1/lims/samples/{id}

Get sample by ID or barcode

POST /api/v1/lims/experiments

Create experiment record

GET /api/v1/lims/audit/{type}/{id}

Get audit trail (compliance)

Quick Start

# Install Science Worker sudo dnf install eldric-scienced-4.0.0-1.el9.x86_64.rpm # Start daemon with AI support sudo systemctl start eldric-scienced # Or run manually with all integrations eldric-scienced --port 8897 \ --controller http://controller:8880 \ --inference-workers http://localhost:8890 \ --agent-workers http://localhost:8893 \ --data-workers http://localhost:8892 \ --gpu --cuda-devices 0,1

Example: AI Analysis of BLAST Results

# First, run a BLAST search curl -X POST http://localhost:8897/api/v1/bio/blast \ -H "Content-Type: application/json" \ -d '{"sequence": "ATGCGATCG...", "database": "nr"}' # Then analyze results with AI curl -X POST http://localhost:8897/api/v1/ai/analyze \ -H "Content-Type: application/json" \ -d '{ "data": { "blast_results": [...] }, "type": "interpret" }' # Response: { "analysis_type": "interpret", "analysis": "The BLAST results show significant homology to...", "model": "llama3.1:70b" }

Example: Generate Research Hypothesis

curl -X POST http://localhost:8897/api/v1/ai/hypothesis \ -H "Content-Type: application/json" \ -d '{ "context": "We observed increased expression of BRCA1 in tumor samples compared to normal tissue. The expression correlated with patient age but not tumor stage..." }' # Response includes ranked hypotheses with confidence scores

Example: Automated Literature Review

curl -X POST http://localhost:8897/api/v1/agent/literature \ -H "Content-Type: application/json" \ -d '{ "topic": "CRISPR-Cas9 off-target effects in therapeutic applications", "max_papers": 20 }' # Agent Worker searches PubMed, arXiv, bioRxiv and generates a comprehensive review

Configuration

{ "port": 8897, "controller_url": "http://controller:8880", "data_worker_urls": ["http://localhost:8892"], "inference_worker_urls": ["http://localhost:8890"], "agent_worker_urls": ["http://localhost:8893"], "llm": { "default_model": "llama3.2:3b", "research_model": "llama3.1:70b", "max_tokens": 4096, "temperature": 0.7, "enable_streaming": false, "timeout_ms": 120000 }, "bioinformatics_backend": "default", "blast_databases_path": "/data/blast_db", "pharma_backend": "default", "pdb_cache_path": "/data/pdb_cache", "lims_database_path": "/data/lims.db", "compliance_mode": "fda_21_cfr_11" }

License Tiers

Feature Free Standard Professional Enterprise
Sequence Analysis
AI Analysis Basic
Agent Delegation
CRISPR Design 5/day 50/day 500/day Unlimited
BLAST Search
Molecular Docking
AlphaFold Integration
LIMS Samples 10 100 1,000 Unlimited
21 CFR Part 11
GPU Acceleration
Science Workers 1 2 5 Unlimited

Port Reference

Component Port Description
Science Worker 8897 Scientific computation + AI research
Controller 8880 Cluster management, licensing
Data Worker 8892 Databases, scientific APIs (62), RAG storage
Inference Worker 8890 LLM inference (Ollama, vLLM, OpenAI...)
Agent Worker 8893 Multi-agent tasks, agentic RAG

Related Documentation