Autonomous AI for Molecular Biology
VigyanLLM is a sovereign biomedical AI platform purpose-built for molecular biology and life sciences research. Unlike cloud-dependent solutions, VigyanLLM runs entirely on your infrastructure — no external API calls, no data leaving your institution, no per-query costs. The platform integrates primer design, CRISPR analysis, molecular docking, BLAST, MSA, and sequence search into a single on-premises workflow. Unlike generic LLM platforms designed for text generation, VigyanLLM is a Biomedical LLM trained on genomic sequences.
Note: VigyanLLM is not a cloud-based SaaS application, nor is it a general-purpose chatbot. It does not process natural language for conversation. VigyanLLM is a specialized, domain-specific biological sequence analysis engine that runs entirely on local institutional hardware.
Autonomous Primer Design Engine
Autonomous primer design is a computational method that uses biophysical validation algorithms to automatically generate oligonucleotide primers for PCR, qPCR, and NGS assays without manual intervention. VigyanLLM's engine combines Primer3 thermodynamic optimization with SantaLucia 1998 nearest-neighbour parameters, BLAST specificity screening, dbSNP variant filtering, repeat masking, and multiplex cross-dimer validation in a single 24-step pipeline.
How VigyanLLM Processes Primer Design
- Input: User provides a target DNA sequence in FASTA format with organism and amplicon range parameters.
- Primer3 Design: The engine runs Primer3 with SantaLucia 1998 nearest-neighbour thermodynamics to generate candidate primer pairs.
- BLAST Specificity: Each candidate is screened against NCBI nucleotide databases to ensure target-specific binding with E-value thresholds.
- Secondary Structure: Hairpin, self-dimer, and cross-dimer free energies are calculated using thermodynamic delta-G predictions.
- SNP & Repeat Filtering: dbSNP variants and repeat elements (LINE, SINE, Alu) within primer binding regions are flagged.
- Multiplex Validation: Cross-primer interactions are scored across all pairs in multiplex panels.
- Output: Ranked primer pairs with pass/fail matrix, Tm, GC%, delta-G, and audit-ready PDF report.
CRISPR Guide RNA (gRNA) Analysis
CRISPR guide RNA (gRNA) analysis is the computational process of identifying optimal single-guide RNA sequences that direct Cas nucleases to specific genomic loci for targeted genome editing. VigyanLLM scans input DNA sequences for PAM motifs (NGG for SpCas9, TTTV for Cas12a), ranks guides by on-target efficiency using Azimuth and DeepCRISPR scoring models, and predicts genome-wide off-target binding using CFD algorithms.
How VigyanLLM Processes CRISPR Guide RNA
- Input: User uploads a target DNA sequence in FASTA format with Cas variant selection.
- PAM Scanning: The local AI model scans for protospacer-adjacent motif sequences (e.g., NGG for SpCas9, TTTV for Cas12a).
- On-Target Scoring: Azimuth 2.0 and DeepCRISPR algorithms calculate on-target cleavage efficiency.
- Off-Target Analysis: CFD scoring across genome-wide mismatch tolerance identifies top off-target sites.
- Output: Ranked list of optimal guide RNAs with specificity scores, exported as CSV for synthesis orders.
GPU-Accelerated Molecular Docking
GPU-accelerated molecular docking is a computational method that uses Graphics Processing Units to simulate the binding of small molecules (ligands) to target protein receptors, significantly reducing computational time compared to CPU-based methods while maintaining binding affinity prediction accuracy.
Cloud vs. On-Premises Bioinformatics
| Feature | Cloud-Based Bioinformatics | VigyanLLM (On-Premises) |
|---|---|---|
| Data Location | Third-party servers (US/EU) | Your local lab servers |
| Data Sovereignty | No — subject to foreign laws | Yes — 100% Indian data sovereignty |
| Internet Required | Yes — always-on connection | No — air-gapped capable |
| Compute Hardware | Shared cloud GPUs (multi-tenant) | Your own dedicated GPUs |
| Per-Query Cost | Pay-per-run or subscription | Zero — unlimited runs after deployment |
Platform Core
🧬 Primer Design Pipeline
Autonomous primer design is a computational method that uses biophysical validation algorithms to automatically generate oligonucleotide primers for PCR, qPCR, and NGS assays. VigyanLLM's 24-step pipeline combines Primer3 thermodynamic optimization with SantaLucia 1998 nearest-neighbour parameters, BLAST specificity screening, dbSNP variant filtering, repeat masking, and multiplex cross-dimer validation.
🤖 Domain-Specific LLM
Domain-specific large language models (LLM) are neural networks trained exclusively on biomedical literature and genomic data rather than general web text. VigyanLLM's VigyanInferenceEngine uses 320,000+ purpose-built biomedical records across 46 specialized training batches with zero external API dependency — every inference is processed locally on your dedicated GPU.
🔬 Protein Structure & Docking
GPU-accelerated molecular docking uses Graphics Processing Units to simulate the binding of small molecules to target protein receptors. VigyanLLM supports AlphaFold, RosettaFold, and ESMFold for structure prediction, and Vina-GPU for docking with binding free energy calculations typically reaching -8.5 kcal/mol for high-affinity interactions.
✂️ CRISPR & Genome Editing
CRISPR guide RNA analysis identifies optimal single-guide RNA sequences that direct Cas nucleases to specific genomic loci. VigyanLLM performs sgRNA-DNA heteroduplex instability analysis, PAM variation mapping (NGG for SpCas9, TTTV for Cas12a), and off-target CFD scoring for precise genome editing with comprehensive validation reports.
Biomedical Research Applications
VigyanLLM supports primer and assay design across the full spectrum of molecular biology applications — from basic PCR and qPCR gene expression analysis to advanced NGS library preparation, CRISPR-Cas9 genotyping, clinical diagnostics, and pharmaceutical drug discovery.
qPCR & RT-PCR
Gene expression, miRNA, viral load
NGS & Sequencing
Amplicon panels, library prep, targeted seq
Genotyping & SNPs
Allele-specific PCR, ARMS, TaqMan assays
Clinical Diagnostics
Pathogen detection, infectious disease, oncology
Biotech & Pharma
Drug discovery, biomarker validation, QC
Forensics & Agri
DNA profiling, GMO testing, breeding
Technical Specifications & Terminology
VigyanLLM's docking engine evaluates ligand-protein binding affinity, calculates RMSD, and supports AutoDock Vina compatible output formats for drug discovery pipelines. Binding free energy scores typically reach -8.5 kcal/mol for high-affinity interactions with MM-GBSA rescoring options.
Our gRNA design pipeline analyzes PAM sequences (e.g., NGG for SpCas9, TTTV for Cas12a), computes on-target scoring (Doench 2016, Azimuth 2.0), and performs off-target mismatch tolerance analysis using CFD algorithms for CRISPR-Cas9 and CRISPR-Cas12a systems with comprehensive heteroduplex instability predictions.
Designed for compliance with Indian data privacy laws, offering an air-gapped deployment alternative to cloud-based SaaS like Benchling, ensuring DPDP Act compliance for genomic data. Supports FASTA, GenBank, and CSV input formats with BAM/VCF integration for variant-aware primer design.
VigyanLLM's architecture uses a multi-agent pipeline with VigyanInferenceEngine running locally via Docker Compose on Ubuntu 22.04 LTS with CUDA-accelerated inference. The platform is air-gapped capable, requires no internet after deployment, and supports LDAP integration for institutional authentication.