DeepChem

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 • 2 shared operations

Use when working with modelangelo — modelAngelo — automated atomic model

1 shared topic • 3 shared operations

MoleculeNet in DeepChem provides curated molecular machine learning dataset loaders for ADME, toxicity, quantum chemistry, materials, and reaction benchmarks through `deepchem.molnet.load_*` functions

1 shared topic • 3 shared operations

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

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