DiVo Gen²AI R&D Services

Compute · Design · Analysis · Insights

Powered by 41+ AI models, covering the full R&D chain from computation to insights.

R&D Service Matrix

Compute

Dry-computing Pipelines

  • Protein Engineering Dry-lab Design
  • Gene Editing System Design
  • mRNA Vaccine/Drug Design
  • AI Drug Virtual Screening
  • Custom Bioinformatics Pipeline Development

Design

Experiment & Scheme Design

  • Mutant Combination Scheme Design
  • gRNA Target & Off-target Assessment
  • UTR/CDS Sequence Optimization
  • Molecular Docking & Binding Mode Prediction
  • Multi-omics Analysis Pipeline Customization

Analysis

Data Analysis & Interpretation

  • WGS/WES Variant Interpretation & VUS Scoring
  • Cancer Full-loop Analysis (Screening→Medication→MRD)
  • Multi-omics Data Integration Analysis
  • Chinese Population Pan-genome Optimized Interpretation
  • Target Discovery & Druggability Assessment

Insights

Insights & Decision Support

  • Multi-model Cross-validation Consensus Report
  • Inter-pipeline Synergy Recommendation
  • Drug R&D Path Planning
  • Precision Medicine Clinical Decision Support
  • Annual AI Re-analysis & Trend Tracking

Main Compute Pipelines

S1

Protein Engineering

From sequence to mutant, 7-step full process. Four-model enzyme activity cross-validation, single-model error 30-50%, trusted only when all four agree.

Coverage95%
2-4 weeks100K-800K/project
S2

Gene Editing System Design

gRNA design + whole-genome off-target scanning + gene optimization. Supports three Cas variants, gene optimization 4.93s/326aa.

Coverage90%
1-2 weeks50K-250K/target
S3

mRNA Vaccine / Drug Design

CDS + 5'UTR + 3'UTR full coverage. GEMORNA three modules, three-tier UTR for different expression needs. Immunogenicity assessed at design stage.

Coverage95%
2-3 weeks150K-500K/project
S4b

Precision Oncology

Genetic risk → screening → medication → MRD monitoring → neoantigen prediction, full loop. VUS triple-model scoring, traditional methods only label "variant of uncertain significance".

Coverage80%
1-3 weeks5K-30K/person
S5

AI Drug Virtual Screening

Target → binding pocket → molecular docking → candidate molecule, end-to-end. TorchANI neural network potential accuracy approaches DFT.

Coverage70%
4-8 weeks300K-1M/project
S6

Custom Bioinformatics Pipeline Development

639 scripts + 11 core model repositories, rapid assembly. Docker containerized delivery, clients can run independently.

Coverage85%
4-12 weeks200K-600K/project

Pipeline Synergy—one project may involve multiple pipelines working together

S4bS3Tumor neoantigen prediction → mRNA vaccine design
S4aS2Pathogenic variant discovery → gene editing plan
S1S3Protein engineering → mRNA antigen structure design
S5S1Drug screening target → protein optimization → candidate drug

Your Needs, Our Drive!

Universities & Research Institutes

Biomedical Enterprises

Hospitals & Public Health Institutions

Government & Regional Projects

Independent Researchers

Our Core Advantages

Multi-model Cross-validation

Four-model enzyme activity cross-validation, VUS triple-model cross-scoring. Single-model error can reach 30-50%, multi-model consensus is trustworthy.

Chinese Population Optimization

Genos trained on 636 Chinese population pan-genomes. HLA-B*1502, ALDH2 and other East Asia-specific high-risk markers are barely covered by Western services.

Flexible Cost Structure

Start with just 1 project, no team needed. Same delivery quality at 1/4 to 1/6 the cost of an in-house team. Pay per project, deliver per result.

Cooperation

Flexible choice, combine as needed.

Computing Service

Contract cooperation, deliver per project!

Per client R&D needs, design compute pipelines and provide computing services and deliverables.

Private Platform Deployment

For institutions needing their own computing capacity

Turn-key compute pipeline platform, annual tech support subscription.

Model Training & Fine-tuning

For institutions needing their own dedicated models

Train or fine-tune dedicated AI models on client data, deliver model weights and inference pipelines.