Rafael I. Brent

RosettaCommons/foundry: v0.1.9

Jue Wang, Nathaniel Corley, Rohith Krishna, J. Laurence Butcher et autres

What's Changed Patch 2 by @timkartar in https://github.com/RosettaCommons/foundry/pull/96 fix: bugs in assertion by @aimura09 in https://github.com/RosettaCommons/foundry/pull/95 docs: update the ppi docs by @RafiBrent in https://github.com/RosettaCommons/foundry/pull/93 fix: include is_non_loopy=true to the example by @aimura09 in https://github.com/RosettaCommons/foundry/pull/99 fix: the cause of the slow inference …

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atomworks

Nathaniel Corley, Simon Mathis, Rohith Krishna, Magnus S. Bauer et autres

A research-oriented data toolkit for training biomolecular deep-learning foundation models. AtomWorks provides tools for parsing, cleaning, manipulating, and converting biological data (structures, sequences, small molecules) as well as advanced dataset featurization and sampling for deep learning workflows.

De novo design of phosphotyrosine peptide binders

Magnus S. Bauer, Jason Z. Zhang, Kejia Wu, Gyu Rie Lee et autres

ABSTRACT Phosphorylation on tyrosine is a key step in many signaling pathways. Despite recent progress in de novo design of protein binders, there are no current methods for designing binders that recognize phosphorylated proteins and peptides; this is a challenging problem as …

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De novo Design of All-atom Biomolecular Interactions with RFdiffusion3

J. Laurence Butcher, Rohith Krishna, Raktim Mitra, Rafael I. Brent et autres

Abstract Deep learning has accelerated protein design, but most existing methods are restricted to generating protein backbone coordinates and often neglect interactions with other biomolecules. We present RFdiffusion3 (RFD3), a diffusion model that generates protein structures in the context of ligands, nucleic …

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Accelerating Biomolecular Modeling with AtomWorks and RF3

Nathaniel Corley, Simon V. Mathis, Rohith Krishna, Magnus S. Bauer et autres

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular …

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ChemSpaceAL: An efficient active learning methodology applied to protein-specific molecular generation

Gregory W. Kyro, Anton Morgunov, Rafael I. Brent, Victor S. Batista

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ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

Gregory W. Kyro, Anton Morgunov, Rafael I. Brent, Víctor S. Batista

The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit …

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ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

Gregory W. Kyro, Anton Morgunov, Rafael I. Brent, Victor S. Batista

The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit …

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein–Ligand Binding Affinity Prediction

Gregory W. Kyro, Rafael I. Brent, Víctor S. Batista

Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting …

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HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction

Gregory W. Kyro, Rafael I. Brent, Victor S. Batista

Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting …

Human germinal centres engage memory and naive B cells after influenza vaccination

Jackson S. Turner, Julian Q. Zhou, Julianna Han, Aaron J. Schmitz et autres

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