SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction-Reference-Cited by-同舟云学术

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SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction

Published:2024-04-30 Issue: Volume: Page:
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Liu Yunjiong¹²^ORCID,Zhang Peiliang³,Che Chao¹²,Wei Ziqi⁴

Affiliation:

1. Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China

2. School of Software Engineering, Dalian University, Dalian 116622, China

3. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China

4. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100864, China

Funder

Department of Education of Liaoning Province

Dalian University of Technology

National Natural Science Foundation of China

Higher Education Discipline Innovation Project

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00177

Reference53 articles.

1. Artificial intelligence techniques for prediction of drug synergy in malignant diseases: Past, present, and future

2. Independent Drug Action in Combination Therapy: Implications for Precision Oncology

3. Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects

4. Combination therapy in combating cancer

5. Randomized Phase II Study of Vandetanib Alone or With Paclitaxel and Carboplatin as First-Line Treatment for Advanced Non–Small-Cell Lung Cancer

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