BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
Abstract
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language models (pLM) training on hundreds of graphical processing units (GPUs). We introduce the BioNeMo Framework to facilitate the training of computational biology and chemistry AI models across hundreds of GPUs. Its modular design allows the integration of individual components, such as data loaders, into existing workflows and is open to community contributions. We detail technical features of the BioNeMo Framework through use cases such as pLM pre-training and fine-tuning. On 256 NVIDIA A100s, BioNeMo Framework trains a three billion parameter BERT-based pLM on over one trillion tokens in 4.2 days. The BioNeMo Framework is open-source and free for everyone to use.
Keywords
Cite
@article{arxiv.2411.10548,
title = {BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery},
author = {Peter St. John and Dejun Lin and Polina Binder and Malcolm Greaves and Vega Shah and John St. John and Adrian Lange and Patrick Hsu and Rajesh Illango and Arvind Ramanathan and Anima Anandkumar and David H Brookes and Akosua Busia and Abhishaike Mahajan and Stephen Malina and Neha Prasad and Sam Sinai and Lindsay Edwards and Thomas Gaudelet and Cristian Regep and Martin Steinegger and Burkhard Rost and Alexander Brace and Kyle Hippe and Luca Naef and Keisuke Kamata and George Armstrong and Kevin Boyd and Zhonglin Cao and Han-Yi Chou and Simon Chu and Allan dos Santos Costa and Sajad Darabi and Eric Dawson and Kieran Didi and Cong Fu and Mario Geiger and Michelle Gill and Darren J Hsu and Gagan Kaushik and Maria Korshunova and Steven Kothen-Hill and Youhan Lee and Meng Liu and Micha Livne and Zachary McClure and Jonathan Mitchell and Alireza Moradzadeh and Ohad Mosafi and Youssef Nashed and Saee Paliwal and Yuxing Peng and Sara Rabhi and Farhad Ramezanghorbani and Danny Reidenbach and Camir Ricketts and Brian C Roland and Kushal Shah and Tyler Shimko and Hassan Sirelkhatim and Savitha Srinivasan and Abraham C Stern and Dorota Toczydlowska and Srimukh Prasad Veccham and Niccolò Alberto Elia Venanzi and Anton Vorontsov and Jared Wilber and Isabel Wilkinson and Wei Jing Wong and Eva Xue and Cory Ye and Xin Yu and Yang Zhang and Guoqing Zhou and Becca Zandstein and Alejandro Chacon and Prashant Sohani and Maximilian Stadler and Christian Hundt and Feiwen Zhu and Christian Dallago and Bruno Trentini and Emine Kucukbenli and Saee Paliwal and Timur Rvachov and Eddie Calleja and Johnny Israeli and Harry Clifford and Risto Haukioja and Nicholas Haemel and Kyle Tretina and Neha Tadimeti and Anthony B Costa},
journal= {arXiv preprint arXiv:2411.10548},
year = {2025}
}