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Continuous Representation of Molecules Using Graph Variational
  Autoencoder

Continuous Representation of Molecules Using Graph Variational Autoencoder

17 April 2020
Mohammadamin Tavakoli
Pierre Baldi
    BDLGNNDRL
ArXiv (abs)PDFHTML

Papers citing "Continuous Representation of Molecules Using Graph Variational Autoencoder"

4 / 4 papers shown
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways
  via Contrastive Learning
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive LearningNeural Information Processing Systems (NeurIPS), 2023
Mohammadamin Tavakoli
Y. T. T. Chiu
Alexander Shmakov
Ann Marie Carlton
David Van Vranken
Pierre Baldi
267
5
0
02 Nov 2023
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction
  Representation
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
Mohammadamin Tavakoli
Alexander Shmakov
Francesco Ceccarelli
Pierre Baldi
GNN
226
11
0
02 Jan 2022
Relational VAE: A Continuous Latent Variable Model for Graph Structured
  Data
Relational VAE: A Continuous Latent Variable Model for Graph Structured Data
Charilaos Mylonas
I. Abdallah
Eleni Chatzi
BDL
158
2
0
30 Jun 2021
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural
  Networks to Predict Chemical Reactivity
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical ReactivityJournal of Chemical Information and Modeling (JCIM), 2021
Mohammadamin Tavakoli
Aaron Mood
David Van Vranken
Pierre Baldi
GNNAI4CE
152
33
0
24 Mar 2021
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