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
🔬 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 Agent Endpoints
POST/api/v1/research/search
Federated search across multiple scientific providers
POST/api/v1/research/conduct
Full research workflow: search → synthesize → report
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
💬
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.
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