mlsanityData to 14 Oct 2026

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery

arXiv 2411.10548 ↗First posted 15 Nov 2024cs.LG q-bio.BMcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 15 Nov 2024.

Builds on

11 of 27 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2020Scaling Laws for Neural Language Modelscited by 6,586
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  6. 2019ZeRO: Memory Optimizations Toward Training Trillion Parameter Modelscited by 1,301
  7. 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 632
  8. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 243
  9. 2019NeMo: a toolkit for building AI applications using Neural Modulescited by 235
  10. 2020ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computingcited by 162
  11. 2024Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 4.

10 papers.

  1. 2025Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labscited by 3
  2. 2025scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omicscited by 1
  3. 2025Contrastive Mutual Information Learning: Toward Robust Representations without Positive-Pair Augmentationscited by 1
  4. 2025Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritizationcited by 1
  5. 2025Best Practices for Biorisk Evaluations on Open-Weight Bio-Foundation Modelscited by 1
  6. 2025Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteinscited by 0
  7. 2026SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation predictioncited by 0
  8. 2026annbatch unlocks terabyte-scale training of biological data in anndatacited by 0
  9. 2026MultiMolecule: a modular ecosystem for biomolecular sequence-model workflowscited by 0
  10. 2026Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutionscited by 0