A New Graph-Based Reinforcement Learning Environment for Targeted Molecular Generation and Optimization✱-Reference-Cited by-同舟云学术

A New Graph-Based Reinforcement Learning Environment for Targeted Molecular Generation and Optimization✱

Published:2023-11-21 Issue: Volume: Page:52-57
ISSN:
Container-title:Proceedings of the 2023 12th International Conference on Software and Information Engineering
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Author:

Mahmoud Amgad Abdallah¹^ORCID,Alyan Nada Adel²^ORCID,Elkerdawy Ahmed³^ORCID,Tanabe Shihori⁴^ORCID,Frederic Andres⁵^ORCID,Pester Andreas⁶^ORCID,Ali Hesham⁷^ORCID

Affiliation:

1. School of Informatics and Computer Science, The British University in Egypt, Egypt

2. School of Informatics and Computer Science, New Giza University, Egypt

3. Cairo University, Egypt

4. National Institute of Health Sciences, Japan

5. National Institute of Informatics, Japan

6. The British University in Egypt, Egypt

7. University of Nebraska at Omaha, United States of America

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3634848.3634857

Reference21 articles.

1. Youngwoo Cho, Sookyung Kim, Peggy Pk Li, Michael P. Surh, Thomas Yong-Jin Han, and Jaegul Choo. 2019. Physics-guided Reinforcement Learning for 3D Molecular Structures. https://api.semanticscholar.org/CorpusID:209334800

2. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds (New Orleans, USA). https://arxiv.org/abs/1903.02428

3. Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments

4. Exploring the Use of Invalid Action Masking in Reinforcement Learning: A Comparative Study of On-Policy and Off-Policy Algorithms in Real-Time Strategy Games

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