MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks-Reference-Cited by-同舟云学术

MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks

Published:2021-07-18 Issue: Volume: Page:
ISSN:
Container-title:2021 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Numeroso Danilo,Bacciu Davide

Funder

MIUR-SIR 2014 LIST-IT

EU Horizon 2020 research and innovation programme

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9533266/9533267/09534266.pdf?arnumber=9534266

Reference41 articles.

1. Deep reinforcement learning for de novo drug design

2. Optimizing distributions over molecular space;sanchez-lengeling;an objective-reinforced generative adversarial network for inverse-design chemistry (organic),2017

3. Multiobjective Reinforcement Learning: A Comprehensive Overview

Cited by 10 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. Learning Counterfactual Explanation of Graph Neural Networks via Generative Flow Network;IEEE Transactions on Artificial Intelligence;2024-09

2. Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

3. Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

4. GRETEL 2.0: Generation and Evaluation of Graph Counterfactual Explanations Evolved;Lecture Notes in Computer Science;2024

5. Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2023-03-01