Automated Molecule Generation using Deep Q-Learning and Graph Neural Networks-Reference-Cited by-同舟云学术

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Automated Molecule Generation using Deep Q-Learning and Graph Neural Networks

Published:2021-12-09 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Isik Riza,Tan Mehmet

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9669261/9669139/09669667.pdf?arnumber=9669667

Reference33 articles.

1. ZINC: A Free Tool to Discover Chemistry for Biology

2. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

3. Extended-Connectivity Fingerprints

4. Deep graph library: A graph-centric, highly-performant package for graph neural networks;wang;arXiv preprint arXiv 1909 01771,2019

5. Molecular generation with recurrent neural networks (rnns);bjerrum;arXiv preprint arXiv 1705 04612,2017

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advancing Drug Discovery with Deep Learning: Harnessing Reinforcement Learning and One-Shot Learning for Molecular Design in Low-Data Situations;ACM SIGAPP Applied Computing Review;2023-03

2. Deep Learning for a Low-Data Drug Design System;2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM);2021-03-01

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