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Found 7 papers, 3 papers with code
Proteina: Scaling Flow-based Protein Structure Generative Models
Here, we develop Proteina, a new large-scale flow-based protein backbone generator that utilizes hierarchical fold class labels for conditioning and relies on a tailored scalable transformer architecture with up to 5x as many parameters as previous models.
BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
We introduce the BioNeMo Framework to facilitate the training of computational biology and chemistry AI models across hundreds of GPUs.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
FLIP: Benchmark tasks in fitness landscape inference for proteins
Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications.
3D Infomax improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
3D Pre-training improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids.
Ranked #1 on
Protein Secondary Structure Prediction
on CASP12
Dimensionality Reduction
Protein Secondary Structure Prediction
