Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation

24 November 2018

Papers citing "Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation"

6 / 6 papers shown

Title
Predicting solvation free energies with an implicit solvent machine learning potential Sebastien Röcken A. F. Burnet J. Zavadlav AI4Cl AI4CE 66 3 0 31 May 2024
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information Taojie Kuang Yiming Ren Zhixiang Ren 23 7 0 28 Sep 2023
Deep Molecular Representation Learning via Fusing Physical and Chemical Information Shuwen Yang Ziyao Li Guojie Song Lingsheng Cai AI4CE 38 28 0 28 Nov 2021
Learning from Protein Structure with Geometric Vector Perceptrons Bowen Jing Stephan Eismann Patricia Suriana Raphael J. L. Townshend R. Dror GNN 3DV 22 470 0 03 Sep 2020
InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation Hyeoncheol Cho E. Lee I. Choi GNN FAtt 12 4 0 12 May 2020
Molecule Attention Transformer Lukasz Maziarka Tomasz Danel Slawomir Mucha Krzysztof Rataj Jacek Tabor Stanislaw Jastrzebski 11 167 0 19 Feb 2020