論文メモ

あとで読もうかなという論文たち

https://www.sciencedirect.com/science/article/abs/pii/S0927025619305026

https://proceedings.mlr.press/v162/stark22a.html

https://pubs.acs.org/doi/10.1021/acssuschemeng.2c05225

https://arxiv.org/abs/2303.08272#

https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00479-8

https://www.nature.com/articles/s43246-022-00315-6

https://arxiv.org/abs/2303.12188

GcNN関連

Make Graph convolution model with geometric deep learning extension library for PyTorch #RDKit #chemoinformatics #pytorch – Is life worth living? (wordpress.com)

HannesStark/3DInfomax: Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric. (github.com)

MolecularAI/GraphINVENT: Graph neural networks for molecular design. (github.com)

Graph Convolutional Network による溶解度予測 (回帰) - Qiita

biomed-AI/MolRep: MolRep: A Deep Representation Learning Library for Molecular Property Prediction (github.com)

DG

GitHub - akensert/molgraph: Graph neural networks for molecular machine learning. Implemented and compatible with TensorFlow and Keras.

GitHub - NU-CUCIS/CheMixNet: Mixed DNN Architectures for Predicting Properties using Multiple Molecular Representations

GitHub - rnepal2/Solubility-Prediction-with-Graph-Neural-Networks: GNN, GCN, Molecular Solubility, RDKit, Cheminformatics

qiitaの方がコード貼り付けやすいかもな。