GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery
Author:
Affiliation:
1. SECNet Lab., Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
2. Department of Computer Science, Texas Tech University, Lubbock, TX, USA
3. Amazon, San Diego, CA, USA
Funder
National Science Foundation
Resilient and Intelligent NextG Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
https://ieeexplore.ieee.org/ielam/8857/10490288/10381739-aam.pdf
Reference50 articles.
1. CrystalGAN: Learning to discover crystallographic structures with generative adversarial networks;Nouira,2018
2. Applications of Deep Learning in Molecule Generation and Molecular Property Prediction
3. FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper
4. Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models;Guimaraes,2017
5. MolGPT: Molecular Generation Using a Transformer-Decoder Model
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