Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8618
Title: Automated Molecule Generation using Deep Q-Learning and Graph Neural Networks
Authors: Isik R.
Tan, Mehmet
Keywords: computational molecule design
deep reinforcement learning
graph neural networks
molecular toxicity
Convolutional neural networks
Deep neural networks
Graph neural networks
Multiobjective optimization
Network architecture
Reinforcement learning
Computational molecule design
Drug discovery
Fingerprint representation
Graph neural networks
Learning neural networks
Molecular representations
Molecular toxicities
Property
Q-learning
Reinforcement learnings
Molecules
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Işık, R., & Tan, M. (2021, December). Automated Molecule Generation using Deep Q-Learning and Graph Neural Networks. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2237-2244). IEEE.
Abstract: The concept of generating molecular structures with specific desirable characteristics underlies some of the crucial problems in drug discovery. In this paper, we present a model which constructs new molecules for specific desired properties. This model uses graph neural networks to generate molecular representations, and combining these representations with Reinforcement Learning architecture (Deep Q-Learning), builds new molecules. We used two different graph neural network architectures: Graph Convolutional Network and Graph Attention Network. We compared the molecular representations obtained from these two models with the Morgan Fingerprint representation in three separate experiments using the same Reinforcement Learning design. These experiments are single-objective optimization (drug-likeness), optimizing the penalized LogP with similarity constraint, and multi-objective optimization (drug-likeness with similarity constraint). The results show that the Reinforcement Learning models trained with molecular representations obtained from graph neural networks are more successful than the model trained with Morgan Fingerprint representation. © 2021 IEEE.
Description: NSF
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 -- 9 December 2021 through 12 December 2021 -- -- 176400
URI: https://doi.org/10.1109/BIBM52615.2021.9669667
https://hdl.handle.net/20.500.11851/8618
ISBN: 9781665401265
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

24
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.