Ibex is a lightweight antibody and TCR structure prediction model.
Ibex can be installed through pip with
pip install prescient-ibexAlternatively, you can use uv and create a new virtual environment
uv venv --python 3.10
source .venv/bin/activate
uv pip install -e .The simplest way to run inference is through the ibex command, e.g.
ibex --fv-heavy EVQLVESGGGLVQPGGSLRLSCAASGFNIKDTYIHWVRQAPGKGLEWVARIYPTNGYTRYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCSRWGGDGFYAMDYWGQGTLVTVSS --fv-light DIQMTQSPSSLSASVGDRVTITCRASQDVNTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSRSGTDFTLTISSLQPEDFATYYCQQHYTTPPTFGQGTKVEIK --output prediction.pdbYou can provide a csv (with the --csv argument) or a parquet file (with the --parquet argument) and run a batched inference writing the output into a specified directory with
ibex --csv sequences.csv --output predictionswhere sequences.csv should contain a fv_heavy and fv_light column with heavy and light chain sequences, and optionally an id column with a string that will be used as part of the output PDB filenames.
By default, structures are predicted in the holo conformation. To predict the apo state, use the --apo flag.
To run a refinement step on the predicted structures, use the --refine option. Additional checks to fix cis-isomers and D-stereoisomers during refinement can be activated with --refine-checks.
Instead of running Ibex, you can use --abodybuilder3 to run inference with the ABodyBuilder3 model.
Below is a summary of all available options:
--abodybuilder3 Use the AbodyBuilder3 model instead of Ibex for inference. [default: no-abodybuilder3]
--fv-heavy Sequence of the heavy chain.
--fv-light Sequence of the light chain.
--csv CSV file containing sequences of heavy and light chains. Columns should be named 'fv_heavy' and 'fv_light'. Output file names can
be provided in a 'id' column.
--parquet Parquet file containing sequences of heavy and light chains. Columns should be named 'fv_heavy' and 'fv_light'. Output file names
can be provided in a 'id' column.
--output Output file for the PDB structure, or path to the output folder when a parquet or csv file is provided. [default: prediction.pdb]
--batch-size Batch size for inference if a parquet or csv file is provided. [default: 32]
--save-all Save all structures of the ensemble as output files. [default: no-save-all]
--refine Refine the output structures with openMM. [default: no-refine]
--refine-checks Additional checks to fix cis-isomers and D-stereoisomers during refinement. [default: no-refine-checks]
--apo Predict structures in the apo conformation. [default: no-apo]
To run Ibex programmatically, you can use
from ibex import Ibex, inference
ibex_model = Ibex.from_pretrained("ibex") # or "abodybuilder3"
inference(ibex_model, fv_heavy, fv_light, "prediction.pdb")to predict structures for multiple sequence pairs, batch_inference is recommended instead of inference.
To predict nanobody structures, leave out the fv_light argument, or set it as "" or None in the csv column.
For inference on TCRs, you should provide the variable beta chain sequence as fv_heavy and the alpha chain as fv_light. Ibex has not been trained on gamma and delta chains.
The Ibex codebase is available under an Apache 2.0 license, and the ABodyBuilder3 model weights under a Creative Commons Attribution 4.0 International license, both of which allow for commercial use.
The Ibex model weights are available under a Genentech Apache 2.0 Non-Commercial license, which allows its use for non-commercial academic research purposes.
Ibex uses as input representation embeddings from ESMC 300M, which is licensed under the EvolutionaryScale Cambrian Open License Agreement.
When using Ibex in your work, please cite the following paper
@misc{ibex,
title={Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins},
author={Frédéric A. Dreyer and Jan Ludwiczak and Karolis Martinkus and Brennan Abanades and Robert G. Alberstein and Pan Kessel and Pranav Rao and Jae Hyeon Lee and Richard Bonneau and Andrew M. Watkins and Franziska Seeger},
year={2025},
eprint={2507.09054},
archivePrefix={arXiv},
primaryClass={q-bio.BM},
url={https://arxiv.org/abs/2507.09054},
}If you use the ABodyBuilder3 model weights, you should also cite
@article{abodybuilder3,
author = {Kenlay, Henry and Dreyer, Frédéric A and Cutting, Daniel and Nissley, Daniel and Deane, Charlotte M},
title = "{ABodyBuilder3: improved and scalable antibody structure predictions}",
journal = {Bioinformatics},
volume = {40},
number = {10},
pages = {btae576},
year = {2024},
month = {10},
issn = {1367-4811},
doi = {10.1093/bioinformatics/btae576}
}