TADA is an AI-powered pipeline for accelerating ADC (Antibody-Drug Conjugate) and TCE (T-cell Engager) drug development, covering both target discovery and antibody engineering.
TADA integrates multi-source data and AI to streamline the discovery and optimization of therapeutic antibodies. The repository is organized into two parts:
- Part I: Antigen target identification and prioritization.
- Part II: In silico antibody design and developability optimization.
The TAA Discovery System is an innovative framework for identifying and prioritizing tumor-associated antigens for TCE or ADC development. It integrates diverse datasets—including multi-omics repositories and scientific publications—using a graph retrieval-augmented generation (RAG)-enhanced language model to extract insights from biological/clinical literature and curated oncology-related omics databases (TCGA, GTEx, single-cell atlases, etc.).
- Graph RAG-enhanced Language Model: Efficiently extracts relevant information from scientific literature.
- Omics Database Integration: Combines data from TCGA, GTEx, and other sources for comprehensive analysis.
- Safety Score: Prioritizes TAAs with high tumor selectivity and low on-target/off-tumor risk.
For methodology and findings, see: Xie & Huang,bioRxiv 2025
After target nomination, antibody engineering remains a major bottleneck. TADA provides an integrated, AI-driven framework for in silico antibody design and multi-objective optimization, combining sequence diversification, structure prediction, docking confidence assessment, and developability scoring.
- Sequence Diversification: Generates CDR-focused antibody variants.
- Structure Prediction: Uses AlphaFold-Multimer and AlphaFold3 for accurate modeling.
- Developability Scoring: Evaluates solubility, CDR liability risks, and interface confidence.
- Unbiased Ranking: Produces a unified score for candidate prioritization.
For methodology and findings, see: Xie, bioRxiv 2026
For questions or collaboration, contact: txie@neoomics.com