TiDE-Ab: De Novo Epitope-Specific Antibody Design via SE(3) Flow Matching

This repository contains the official implementation of the paper: "De Novo Epitope-Specific Antibody Design via SE(3) Flow Matching with Time-Dependent Guidance". TiDE-Ab is a generative framework for the de novo design of therapeutic antibodies targeting specific epitopes. By leveraging Conditional SE(3) Flow Matching and a novel Time-Dependent Classifier-Free Guidance (TD-CFG) strategy, TiDE-Ab generates physically plausible antibody backbones and accurate global binding poses without relying on pre-aligned templates.

Data Preparation

The model is trained on antibody-antigen complexes sourced from the SAbDab database, with a temporal cutoff of April 30, 2020. The dataset is managed through metadata files located in data/splits/.

├── data
│   └── splits          # Dataset split metadata
│       ├── metadata_train.csv
│       ├── metadata_val.csv
│       └── metadata_test.csv

The metadata files in data/splits/ follow this schema:

Column	Description
pdb_name	Unique identifier for the complex (e.g., 1yy9_D_C_A).
num_chains	Total number of chains in the structure.
seq_len	Total sequence length of the complex.
cluster	Interaction cluster ID used for balanced sampling.

Running the Code

Training

To start training with the default configuration:

python train.py

You can override any parameter directly from the command line using dot notation:

python train.py optimizer.lr=0.0005

Inference

1. Download Pre-trained Weights

First, download the pre-trained model weights and place the file in the checkpoints/ directory:

• weights.pt.

2. Run Inference on Test Set

To generate structures on the test set using the downloaded checkpoint, run the following command:

python inference.py

Acknowledgements

This codebase is developed based on the FrameFlow repository. We thank the original authors for their pioneering work on $SE(3)$ flow matching for protein structures.