graphein

OpenChem is a deep learning toolkit for Computational Chemistry with PyTorch backend.

Deep learning library for Chemistry based on Tensorflow

E(3)-equivariant neural network interatomic potentials achieving DFT accuracy with up to 1000× less training data than invariant models, foundational architecture behind MACE and Allegro (Harvard, MIT, Nature Communications 2022)

A client to simplify fetching predictions from the Koina web service. Koina is a model repository enabling the remote execution of models. Predictions are generated as a response to HTTP/S requests, the standard protocol used for nearly all web traffic.

DoGSiteScorer is a grid-based automated pocket detection and analysis tool. It applies a Difference of Gaussian filter to detect potential binding pockets and splits them into sub-pockets. The method solely uses the 3D structure of the protein. Global properties, describing the size, shape, and chemical features of the predicted (sub-)pockets, are calculated. Per default, a simple druggability score based on a linear combination of the three descriptors describing volume, hydrophobicity, and enclosure is provided for each (sub-)pocket. Furthermore, a subset of meaningful descriptors is incorporated in a support vector machine (libsvm) to predict the (sub-)pocket druggability score (values are between zero and one). The higher the score, the more druggable the pocket is estimated to be.

Cross-platform library for differentiable programming of quantum computers with automatic differentiation, enabling hybrid quantum-classical machine learning for quantum chemistry, quantum physics, and NISQ algorithm research (Xanadu, 3k+ stars)