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AI-Powered Biopharmaceutical R&D

In today's era of deep integration between artificial intelligence and biotechnology, Creative Biogene introduces a comprehensive AI service matrix covering the full spectrum of research and development needs, including antibody engineering, gene editing, enzyme optimization, and bioprocess development. Based on our proprietary algorithms and industry-leading wet lab validation systems, we are committed to providing global research institutions and pharmaceutical companies with precise, efficient, and translatable AI solutions.

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AI-Driven Gene Editing and Therapy

1. Intelligent mRNA Sequence

Optimization Deep Learning Pipeline

Integrates ORF codon usage optimization (CAI>0.95), UTR dynamic structure prediction (RNA-Bart model), and GC content balancing (35%-65%), achieving a 5-8-fold increase in protein expression.

Experimental Validation Module

Ensures optimization consistency through CHO cell transient transfection and ELISA quantification.

2. Gene Editing Vector Design

sgRNA off-target prediction

Employs Convolutional Neural Networks (CNN) to analyze genome-wide potential cutting sites, achieving specificity scores >90%.

Lentiviral vector optimization

Enhances transduction efficiency to >95% through promoter activity prediction models and integration site preference analysis.

Multi-species gene editing

Supports multi-species gene knockout/knock-in (including primary cells and iPSCs) with a 6-week delivery timeline.

3. Stable Cell Line Development

Trains LSTM models on metabolomics data to dynamically regulate media components (e.g., glucose/glutamine ratio), achieving a 3-fold increase in CHO cell antibody expression.

Provides monoclonality verification (via microfluidic sorting) and genetic stability testing (>15 continuous passages).

AI-Antibody Engineering Fusion

Epitope-Guided Antibody Design

Integrates deep characterization of natural antibody libraries with bidirectional LSTM-CRF hybrid models for de novo design and optimization of CDR regions, generating millions of candidate molecules and screening for high-potential antibodies with binding energies below -9.0 kcal/mol.

Combines AlphaFold2 and RosettaFold to predict antibody-antigen complex 3D structures, precisely identifying epitope binding hotspots and optimizing binding interface charge complementarity.

Dynamic Affinity Maturation

Employs Graph Neural Networks (GNN) to construct 5-mer, 7-mer, and 9-mer virtual libraries, combined with Molecular Dynamics (MD) simulations for conformational stability assessment, achieving 4x higher hit rates than traditional phage display.

Supports multi-parameter optimization: solubility, immunogenicity, and thermal stability.

Antibody Function Validation Loop Wet Lab Integration

Validates AI predictions through Surface Plasmon Resonance (SPR) and cell-killing assays, ensuring consistency between binding affinity (KD<1nM) and functional activity (EC50<10nM). Provides downstream engineering services including IgG subtype conversion and Fc fucosylation modification to enhance ADCC/ADCP effects.

AI-Driven Enzyme Engineering

Supports activity-stability-solubility triple screening of million-scale mutant libraries, improving positive rates from 0.1% to 12%.

Predicts enzyme stability in extreme temperatures (>70°C) or organic solvents with RMSD error<0.15 Å.

ASRA Algorithm enhances stereoselectivity to >99% through an adaptive base substitution strategy.

Combines microfluidic chip validation, reducing iteration cycles.

AI-Enhanced Small Molecule Screening

01

Intelligent Target Profiling

  • Al identifies binding pockets.
  • Dynamic molecular library engineering.
  • Enhanced virtual compound library.
02

Adaptive Al Screening Cascade

  • Smart filtering architecture.
  • Transformer models with dynamics.
  • SHAP-guided structure optimization.
03

Wet-Dry Cycle Validation

  • Al-driven experimental module.
  • Smart experiment scheduling.
  • lterative optimization milestones.

Technology Ecosystem and Collaboration Network

Creative Biogene's AI services form a complete cycle of algorithm development-wet lab validation-industrial implementation:

Algorithm Layer

Proprietary framework supporting TensorFlow/PyTorch hybrid architecture, compatible with open-source tools like AlphaFold2.

Data Layer

Houses 200,000+ antibody sequences, 50,000+ enzyme mutant databases, and 100,000+ fermentation process parameters.

Hardware Layer

Equipped with NVIDIA DGX A100 clusters and automated high-throughput screening.

Contact us today to obtain customized AI solutions and accelerate your biopharmaceutical R&D process! All services are ISO 9001 and GLP certified, with data security compliant with international standards.

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