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Main Authors:	John, Peter St., Lin, Dejun, Binder, Polina, Greaves, Malcolm, Shah, Vega, John, John St., Lange, Adrian, Hsu, Patrick, Illango, Rajesh, Ramanathan, Arvind, Anandkumar, Anima, Brookes, David H, Busia, Akosua, Mahajan, Abhishaike, Malina, Stephen, Prasad, Neha, Sinai, Sam, Edwards, Lindsay, Gaudelet, Thomas, Regep, Cristian, Steinegger, Martin, Rost, Burkhard, Brace, Alexander, Hippe, Kyle, Naef, Luca, Kamata, Keisuke, Armstrong, George, Boyd, Kevin, Cao, Zhonglin, Chou, Han-Yi, Chu, Simon, Costa, Allan dos Santos, Darabi, Sajad, Dawson, Eric, Didi, Kieran, Fu, Cong, Geiger, Mario, Gill, Michelle, Hsu, Darren J, Kaushik, Gagan, Korshunova, Maria, Kothen-Hill, Steven, Lee, Youhan, Liu, Meng, Livne, Micha, McClure, Zachary, Mitchell, Jonathan, Moradzadeh, Alireza, Mosafi, Ohad, Nashed, Youssef, Paliwal, Saee, Peng, Yuxing, Rabhi, Sara, Ramezanghorbani, Farhad, Reidenbach, Danny, Ricketts, Camir, Roland, Brian C, Shah, Kushal, Shimko, Tyler, Sirelkhatim, Hassan, Srinivasan, Savitha, Stern, Abraham C, Toczydlowska, Dorota, Veccham, Srimukh Prasad, Venanzi, Niccolò Alberto Elia, Vorontsov, Anton, Wilber, Jared, Wilkinson, Isabel, Wong, Wei Jing, Xue, Eva, Ye, Cory, Yu, Xin, Zhang, Yang, Zhou, Guoqing, Zandstein, Becca, Chacon, Alejandro, Sohani, Prashant, Stadler, Maximilian, Hundt, Christian, Zhu, Feiwen, Dallago, Christian, Trentini, Bruno, Kucukbenli, Emine, Rvachov, Timur, Calleja, Eddie, Israeli, Johnny, Clifford, Harry, Haukioja, Risto, Haemel, Nicholas, Tretina, Kyle, Tadimeti, Neha, Costa, Anthony B
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Biomolecules
Online Access:	https://arxiv.org/abs/2411.10548
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_version_	1866916940115607552
author	John, Peter St. Lin, Dejun Binder, Polina Greaves, Malcolm Shah, Vega John, John St. Lange, Adrian Hsu, Patrick Illango, Rajesh Ramanathan, Arvind Anandkumar, Anima Brookes, David H Busia, Akosua Mahajan, Abhishaike Malina, Stephen Prasad, Neha Sinai, Sam Edwards, Lindsay Gaudelet, Thomas Regep, Cristian Steinegger, Martin Rost, Burkhard Brace, Alexander Hippe, Kyle Naef, Luca Kamata, Keisuke Armstrong, George Boyd, Kevin Cao, Zhonglin Chou, Han-Yi Chu, Simon Costa, Allan dos Santos Darabi, Sajad Dawson, Eric Didi, Kieran Fu, Cong Geiger, Mario Gill, Michelle Hsu, Darren J Kaushik, Gagan Korshunova, Maria Kothen-Hill, Steven Lee, Youhan Liu, Meng Livne, Micha McClure, Zachary Mitchell, Jonathan Moradzadeh, Alireza Mosafi, Ohad Nashed, Youssef Paliwal, Saee Peng, Yuxing Rabhi, Sara Ramezanghorbani, Farhad Reidenbach, Danny Ricketts, Camir Roland, Brian C Shah, Kushal Shimko, Tyler Sirelkhatim, Hassan Srinivasan, Savitha Stern, Abraham C Toczydlowska, Dorota Veccham, Srimukh Prasad Venanzi, Niccolò Alberto Elia Vorontsov, Anton Wilber, Jared Wilkinson, Isabel Wong, Wei Jing Xue, Eva Ye, Cory Yu, Xin Zhang, Yang Zhou, Guoqing Zandstein, Becca Chacon, Alejandro Sohani, Prashant Stadler, Maximilian Hundt, Christian Zhu, Feiwen Dallago, Christian Trentini, Bruno Kucukbenli, Emine Paliwal, Saee Rvachov, Timur Calleja, Eddie Israeli, Johnny Clifford, Harry Haukioja, Risto Haemel, Nicholas Tretina, Kyle Tadimeti, Neha Costa, Anthony B
author_facet	John, Peter St. Lin, Dejun Binder, Polina Greaves, Malcolm Shah, Vega John, John St. Lange, Adrian Hsu, Patrick Illango, Rajesh Ramanathan, Arvind Anandkumar, Anima Brookes, David H Busia, Akosua Mahajan, Abhishaike Malina, Stephen Prasad, Neha Sinai, Sam Edwards, Lindsay Gaudelet, Thomas Regep, Cristian Steinegger, Martin Rost, Burkhard Brace, Alexander Hippe, Kyle Naef, Luca Kamata, Keisuke Armstrong, George Boyd, Kevin Cao, Zhonglin Chou, Han-Yi Chu, Simon Costa, Allan dos Santos Darabi, Sajad Dawson, Eric Didi, Kieran Fu, Cong Geiger, Mario Gill, Michelle Hsu, Darren J Kaushik, Gagan Korshunova, Maria Kothen-Hill, Steven Lee, Youhan Liu, Meng Livne, Micha McClure, Zachary Mitchell, Jonathan Moradzadeh, Alireza Mosafi, Ohad Nashed, Youssef Paliwal, Saee Peng, Yuxing Rabhi, Sara Ramezanghorbani, Farhad Reidenbach, Danny Ricketts, Camir Roland, Brian C Shah, Kushal Shimko, Tyler Sirelkhatim, Hassan Srinivasan, Savitha Stern, Abraham C Toczydlowska, Dorota Veccham, Srimukh Prasad Venanzi, Niccolò Alberto Elia Vorontsov, Anton Wilber, Jared Wilkinson, Isabel Wong, Wei Jing Xue, Eva Ye, Cory Yu, Xin Zhang, Yang Zhou, Guoqing Zandstein, Becca Chacon, Alejandro Sohani, Prashant Stadler, Maximilian Hundt, Christian Zhu, Feiwen Dallago, Christian Trentini, Bruno Kucukbenli, Emine Paliwal, Saee Rvachov, Timur Calleja, Eddie Israeli, Johnny Clifford, Harry Haukioja, Risto Haemel, Nicholas Tretina, Kyle Tadimeti, Neha Costa, Anthony B
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2411_10548
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery John, Peter St. Lin, Dejun Binder, Polina Greaves, Malcolm Shah, Vega John, John St. Lange, Adrian Hsu, Patrick Illango, Rajesh Ramanathan, Arvind Anandkumar, Anima Brookes, David H Busia, Akosua Mahajan, Abhishaike Malina, Stephen Prasad, Neha Sinai, Sam Edwards, Lindsay Gaudelet, Thomas Regep, Cristian Steinegger, Martin Rost, Burkhard Brace, Alexander Hippe, Kyle Naef, Luca Kamata, Keisuke Armstrong, George Boyd, Kevin Cao, Zhonglin Chou, Han-Yi Chu, Simon Costa, Allan dos Santos Darabi, Sajad Dawson, Eric Didi, Kieran Fu, Cong Geiger, Mario Gill, Michelle Hsu, Darren J Kaushik, Gagan Korshunova, Maria Kothen-Hill, Steven Lee, Youhan Liu, Meng Livne, Micha McClure, Zachary Mitchell, Jonathan Moradzadeh, Alireza Mosafi, Ohad Nashed, Youssef Paliwal, Saee Peng, Yuxing Rabhi, Sara Ramezanghorbani, Farhad Reidenbach, Danny Ricketts, Camir Roland, Brian C Shah, Kushal Shimko, Tyler Sirelkhatim, Hassan Srinivasan, Savitha Stern, Abraham C Toczydlowska, Dorota Veccham, Srimukh Prasad Venanzi, Niccolò Alberto Elia Vorontsov, Anton Wilber, Jared Wilkinson, Isabel Wong, Wei Jing Xue, Eva Ye, Cory Yu, Xin Zhang, Yang Zhou, Guoqing Zandstein, Becca Chacon, Alejandro Sohani, Prashant Stadler, Maximilian Hundt, Christian Zhu, Feiwen Dallago, Christian Trentini, Bruno Kucukbenli, Emine Paliwal, Saee Rvachov, Timur Calleja, Eddie Israeli, Johnny Clifford, Harry Haukioja, Risto Haemel, Nicholas Tretina, Kyle Tadimeti, Neha Costa, Anthony B Machine Learning Biomolecules 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.
title	BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
topic	Machine Learning Biomolecules
url	https://arxiv.org/abs/2411.10548

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