chang-bwl/iglm

Generative Language Modeling for Antibody Design

IgLM

Official repository for IgLM: Generative Language Modeling for Antibody Design

The code and pre-trained models from this work are made available for non-commercial use under the terms of the JHU Academic Software License Agreement.

Setup

To use IgLM, install dependencies using conda:

conda env create -f environment.yml

Usage

Re-design spans of an antibody sequence

To use IgLM to re-design spans of an antibody sequence, supply the fasta file, the fasta record ID corresponding to the sequence to design, the start index of the span (0-indexed), and the end index of the span (0-indexed, exclusive).

To generate 100 unique sequences of the anti-tissue factor antibody (1JPT) heavy chain with an IgLM-designed CDR3:

python infill.py data/antibodies/1jpt/1jpt.fasta :H 98 106 --chain_token [HEAVY] --species_token [HUMAN] --num_seqs 100 

Full antibody sequence generation

IgLM can be used to generate full antibody sequences while conditioning on the chain type and species-of-origin. See Appendix A.5 for starting tokens and sampling temperatures used for the results in the paper.

To generate 100 unique human heavy chain sequences starting with EVQ:

python full_generation.py --prompt_tokens EVQ --chain_token [HEAVY] --species_token [HUMAN] --num_seqs 100 

To generate 100 unique nanobody sequences starting with QVQ:

python full_generation.py --prompt_tokens QVQ --chain_token [HEAVY] --species_token [CAMEL] --num_seqs 100