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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deep learning improves prediction of drug–drug and drug–food interactions
Jae Yong Ryu, Hyun Uk Kim, Sang Yup Lee
Proceedings of the National Academy of Sciences (2018) Vol. 115, Iss. 18
Open Access | Times Cited: 450

Showing 1-25 of 450 citing articles:

Artificial intelligence in drug development: present status and future prospects
Kit‐Kay Mak, Mallikarjuna Rao Pichika
Drug Discovery Today (2018) Vol. 24, Iss. 3, pp. 773-780
Closed Access | Times Cited: 662
An Introduction to Machine Learning
Solveig Badillo, Balázs Bánfai, Fabian Birzele, et al.
Clinical Pharmacology & Therapeutics (2020) Vol. 107, Iss. 4, pp. 871-885
Open Access | Times Cited: 539
Graph embedding on biomedical networks: methods, applications and evaluations
Xiang Yue, Zhen Wang, Jingong Huang, et al.
Bioinformatics (2019) Vol. 36, Iss. 4, pp. 1241-1251
Open Access | Times Cited: 346
A multimodal deep learning framework for predicting drug–drug interaction events
Yifan Deng, Xinran Xu, Yang Qiu, et al.
Bioinformatics (2020) Vol. 36, Iss. 15, pp. 4316-4322
Closed Access | Times Cited: 293
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
Marinka Żitnik, Francis Nguyen, Bo Wang, et al.
Information Fusion (2018) Vol. 50, pp. 71-91
Open Access | Times Cited: 285
Inhibition and induction of CYP enzymes in humans: an update
Jukka Hakkola, Janne Hukkanen, Miia Turpeinen, et al.
Archives of Toxicology (2020) Vol. 94, Iss. 11, pp. 3671-3722
Open Access | Times Cited: 274
Constrained Bayesian optimization for automatic chemical design using variational autoencoders
Ryan‐Rhys Griffiths, José Miguel Hernández-Lobato
Chemical Science (2019) Vol. 11, Iss. 2, pp. 577-586
Open Access | Times Cited: 273
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Sezen Vatansever, Avner Schlessinger, Daniel Wacker, et al.
Medicinal Research Reviews (2020) Vol. 41, Iss. 3, pp. 1427-1473
Open Access | Times Cited: 257
Opening the black box of neural networks: methods for interpreting neural network models in clinical applications
Zhongheng Zhang, Marcus W. Beck, David A. Winkler, et al.
Annals of Translational Medicine (2018) Vol. 6, Iss. 11, pp. 216-216
Open Access | Times Cited: 237
To Embed or Not: Network Embedding as a Paradigm in Computational Biology
Walter Nelson, Marinka Żitnik, Bo Wang, et al.
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 167
Toward better drug discovery with knowledge graph
Xiangxiang Zeng, Xinqi Tu, Yuansheng Liu, et al.
Current Opinion in Structural Biology (2021) Vol. 72, pp. 114-126
Closed Access | Times Cited: 159
SSI–DDI: substructure–substructure interactions for drug–drug interaction prediction
Arnold K Nyamabo, Hui Yu, Jian‐Yu Shi
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 151
Pharmacodynamic Drug–Drug Interactions
Jin Niu, Robert M. Straubinger, Donald E. Mager
Clinical Pharmacology & Therapeutics (2019) Vol. 105, Iss. 6, pp. 1395-1406
Open Access | Times Cited: 150
MUFFIN: multi-scale feature fusion for drug–drug interaction prediction
Yujie Chen, Tengfei Ma, Xixi Yang, et al.
Bioinformatics (2021) Vol. 37, Iss. 17, pp. 2651-2658
Closed Access | Times Cited: 140
SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization
Yue Yu, Kexin Huang, Chao Zhang, et al.
Bioinformatics (2021) Vol. 37, Iss. 18, pp. 2988-2995
Open Access | Times Cited: 112
MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism
Shenggeng Lin, Yanjing Wang, Lingfeng Zhang, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 111
An effective self-supervised framework for learning expressive molecular global representations to drug discovery
Pengyong Li, Jun Wang, Yixuan Qiao, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 101
A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning
Ke Han, Peigang Cao, Yu Wang, et al.
Frontiers in Pharmacology (2022) Vol. 12
Open Access | Times Cited: 98
On the road to explainable AI in drug-drug interactions prediction: A systematic review
Thanh Hoa Vo, Ngan Nguyen, Quang Hien Kha, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 2112-2123
Open Access | Times Cited: 88
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Xuan Lin, Lihua Dai, Yafang Zhou, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Open Access | Times Cited: 45
Data, measurement and empirical methods in the science of science
Lu Liu, Benjamin F. Jones, Brian Uzzi, et al.
Nature Human Behaviour (2023) Vol. 7, Iss. 7, pp. 1046-1058
Open Access | Times Cited: 42
A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
Mei Ma, Xiujuan Lei
PLoS Computational Biology (2023) Vol. 19, Iss. 1, pp. e1010812-e1010812
Open Access | Times Cited: 41
Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug–Food Interactions from Chemical Structures
Quang-Hien Kha, Viet-Huan Le, Truong Nguyen Khanh Hung, et al.
Sensors (2023) Vol. 23, Iss. 8, pp. 3962-3962
Open Access | Times Cited: 40
MIFS: An adaptive multipath information fused self-supervised framework for drug discovery
Xu Gong, Qun Liu, Rui Han, et al.
Neural Networks (2025) Vol. 184, pp. 107088-107088
Closed Access | Times Cited: 1
A Multi-View Feature-Based Interpretable Deep Learning Framework for Drug-Drug Interaction Prediction
Zihui Cheng, Zhaojing Wang, Xianfang Tang, et al.
Interdisciplinary Sciences Computational Life Sciences (2025)
Closed Access | Times Cited: 1
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