Orcasound

DeepChem — Python framework for deep learning in drug discovery, materials science, quantum chemistry, and biology. Provides molecular featurizers (ECFP, graph convolutions, Coulomb matrices), pre-bui

2 shared topics • 1 shared operation

DeepSEA — deep learning framework for predicting chromatin effects of sequence alterations at single-nucleotide resolution. Trains convolutional neural networks on 919 chromatin features (TF binding,

2 shared topics • 1 shared operation

GNNExplainer explains predictions made by graph neural networks by learning sparse masks over subgraph structure and node features. Use for post-hoc GNN interpretability, identifying important edges a

2 shared topics • 1 shared operation

ProteinMPNN — deep learning-based protein sequence design from backbone structures. Uses message passing neural networks to predict amino acid sequences that fold into a given 3D backbone. Supports fi

2 shared topics • 1 shared operation

Use when working with PyG (PyTorch Geometric) — the PyTorch-based library for deep learning on graphs and irregular data structures. Supports graph neural networks (GNNs) including GCN, GAT, GraphSAGE

2 shared topics • 1 shared operation