ArticleNature communications2022
PyUUL provides an interface between biological structures and deep learning algorithms.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Atomic structures of medin and Aβ fibrils reveal polymorphic remodeling in mixed amyloid systems.Nature communications · 2026Article
- SpheronizaTor: Spherical Voxelization for Interpretable Protein Microenvironment Modeling.Computational and structural biotechnology journal · 2026Article
- Medin drives Aβ40 to adopt Aβ42-like fibril polymorphs in vitro.bioRxiv : the preprint server for biology · 2025Article
- Deep generative model for protein subcellular localization prediction.Briefings in bioinformatics · 2025Article
- SFCalculator: connecting deep generative models and crystallography.bioRxiv : the preprint server for biology · 2025Article
- Artificial intelligence in drug development.Nature medicine · 2025Review
- Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana.Scientific reports · 2024Article
- Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.Current medicinal chemistry · 2024Review
- Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients.Scientific reports · 2023Article
- HPMPdb: A machine learning-ready database of protein molecular phenotypes associated to human missense variants.Current research in structural biology · 2022Article
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6 authors.
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Abstract
Structural bioinformatics suffers from the lack of interfaces connecting biological structures and machine learning methods, making the application of modern neural network architectures impractical. This negatively affects the development of structure-based bioinformatics methods, causing a bottleneck in biological research. Here we present PyUUL ( https://pyuul.readthedocs.io/ ), a library to translate biological structures into 3D tensors, allowing an out-of-the-box application of state-of-the-art deep learning algorithms. The library converts biological macromolecules to data structures typical of computer vision, such as voxels and point clouds, for which extensive machine learning research has been performed. Moreover, PyUUL allows an out-of-the box GPU and sparse calculation. Finally, we demonstrate how PyUUL can be used by researchers to address some typical bioinformatics problems, such as structure recognition and docking.
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