Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

A review of machine learning approaches for drug synergy prediction in cancer
Anna Torkamannia, Yadollah Omidi, Reza Ferdousi
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction
Fatemeh Rafiei, Hojjat Zeraati, Karim Abbasi, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 7, pp. 2577-2585
Closed Access | Times Cited: 28
Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance
Angela Cesaro, Samuel C. Hoffman, Payel Das, et al.
npj Antimicrobials and Resistance (2025) Vol. 3, Iss. 1
Open Access | Times Cited: 7
Predicting drug combination response surfaces
Riikka Huusari, Tianduanyi Wang, Sándor Szedmák, et al.
npj Drug Discovery. (2025) Vol. 2, Iss. 1
Open Access | Times Cited: 2
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Magdalena Wysocka, Oskar Wysocki, Marie Zufferey, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 30
Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy
Alireza Poustforoosh
Molecular Diversity (2025)
Closed Access | Times Cited: 1
MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders
Peng Zhang, Shikui Tu
PLoS Computational Biology (2023) Vol. 19, Iss. 3, pp. e1010951-e1010951
Open Access | Times Cited: 21
Drug discovery and development in the era of artificial intelligence: From machine learning to large language models
Shenghui Guan, Guanyu Wang
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 1, pp. 100070-100070
Open Access | Times Cited: 8
A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction
Lea Eckhart, Kerstin Lenhof, Lisa-Marie Rolli, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 6
Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation
Jingbo Zhou, Dongyang Tang, Lin He, et al.
Pharmacological Research (2023) Vol. 194, pp. 106830-106830
Open Access | Times Cited: 16
The recent progress of deep-learning-based in silico prediction of drug combination
Haoyang Liu, Zhiguang Fan, Jie Lin, et al.
Drug Discovery Today (2023) Vol. 28, Iss. 7, pp. 103625-103625
Closed Access | Times Cited: 15
SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy
Anna Torkamannia, Yadollah Omidi, Reza Ferdousi
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 12
A review on graph neural networks for predicting synergistic drug combinations
Milad Besharatifard, Fatemeh Vafaee
Artificial Intelligence Review (2024) Vol. 57, Iss. 3
Open Access | Times Cited: 4
Scaling up drug combination surface prediction
Riikka Huusari, Tianduanyi Wang, Sándor Szedmák, et al.
Briefings in Bioinformatics (2025) Vol. 26, Iss. 2
Open Access
MGTNSyn: Molecular structure-aware graph transformer network with relational attention for drug synergy prediction
Yunjiong Liu, Peiliang Zhang, Dongyang Li, et al.
Expert Systems with Applications (2025), pp. 127699-127699
Closed Access
Combination Therapies in Drug Repurposing: Personalized Approaches to Combatting Leukaemia and Multiple Myeloma
Bernice A. Monchusi, Pritam N. Dube, Mutsa M. Takundwa, et al.
Advances in experimental medicine and biology (2025)
Closed Access
Translational Bioinformatics Applied to the Study of Complex Diseases
Matheus Correia Casotti, Débora Dummer Meira, Lyvia Neves Rebello Alves, et al.
Genes (2023) Vol. 14, Iss. 2, pp. 419-419
Open Access | Times Cited: 10
Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction
Peng Zheng, Yanling Ding, Pengfei Zhang, et al.
Current Medicinal Chemistry (2024) Vol. 31, Iss. 40, pp. 6572-6585
Closed Access | Times Cited: 2
Harnessing Machine Learning Potential for Personalised Drug Design and Overcoming Drug Resistance
Mohammed Ageeli Hakami
Journal of drug targeting (2024) Vol. 32, Iss. 8, pp. 918-930
Closed Access | Times Cited: 2
Escin enhanced the efficacy of sorafenib by autophagy‐mediated apoptosis in lung cancer cells
Yusuf Hussain, Jyoti Singh, Abha Meena, et al.
Phytotherapy Research (2023) Vol. 37, Iss. 10, pp. 4819-4837
Closed Access | Times Cited: 4
3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors
Marina Gorostiola González, Remco L. van den Broek, Thomas Braun, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 4
SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction
Yunjiong Liu, Peiliang Zhang, Chao Che, et al.
Journal of Chemical Information and Modeling (2024)
Closed Access | Times Cited: 1
Insights from Augmented Data Integration and Strong Regularization in Drug Synergy Prediction with SynerGNet
Mengmeng Liu, Gopal Srivastava, J. Ramanujam, et al.
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 3, pp. 1782-1797
Open Access | Times Cited: 1
Reveal the potent antidepressant effects of Zhi-Zi-Hou-Pu Decoction based on integrated network pharmacology and DDI analysis by deep learning
Zhiwen Zhang, Xiaojing Li, Zihui Huang, et al.
Heliyon (2024) Vol. 10, Iss. 22, pp. e38726-e38726
Open Access | Times Cited: 1
A multi-task graph deep learning model to predict drugs combination of synergy and sensitivity scores
Samar Monem, Aboul Ella Hassanien, Alaa H. Abdel‐Hamid
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 1
A knowledge graph embedding-based method for predicting the synergistic effects of drug combinations
Peng Zhang, Shikui Tu
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2022), pp. 1974-1981
Closed Access | Times Cited: 7
Page 1 - Next Page

Scroll to top