PyUUL provides an interface between biological structures and deep learning algorithms

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.

Automated summary

Structural bioinformatics lacks interfaces connecting with machine learning methods, hindering the application of modern neural network architectures. PyUUL is introduced as a library that translates biological structures into 3D tensors, enabling the application of state-of-the-art deep learning algorithms. The library also supports GPU and sparse calculation, and can be used to address typical bioinformatics problems.