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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 Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
Alexander Aliper, Sergey M. Plis, Artem V. Artemov, et al.
Molecular Pharmaceutics (2016) Vol. 13, Iss. 7, pp. 2524-2530
Open Access | Times Cited: 530

Showing 1-25 of 530 citing articles:

Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel Himmelstein, Brett K. Beaulieu‐Jones, et al.
Journal of The Royal Society Interface (2018) Vol. 15, Iss. 141, pp. 20170387-20170387
Open Access | Times Cited: 1866
The rise of deep learning in drug discovery
Hongming Chen, Ola Engkvist, Yinhai Wang, et al.
Drug Discovery Today (2018) Vol. 23, Iss. 6, pp. 1241-1250
Open Access | Times Cited: 1481
Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, Alexander Tropsha
Science Advances (2018) Vol. 4, Iss. 7
Open Access | Times Cited: 1165
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: 691
Automating drug discovery
Gisbert Schneider
Nature Reviews Drug Discovery (2017) Vol. 17, Iss. 2, pp. 97-113
Open Access | Times Cited: 667
From machine learning to deep learning: progress in machine intelligence for rational drug discovery
Lu Zhang, Jianjun Tan, Dan Han, et al.
Drug Discovery Today (2017) Vol. 22, Iss. 11, pp. 1680-1685
Closed Access | Times Cited: 666
Rethinking drug design in the artificial intelligence era
Petra Schneider, W. Patrick Walters, Alleyn T. Plowright, et al.
Nature Reviews Drug Discovery (2019) Vol. 19, Iss. 5, pp. 353-364
Open Access | Times Cited: 661
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy, Alexander Zhebrak, Benjamín Sánchez-Lengeling, et al.
Frontiers in Pharmacology (2020) Vol. 11
Open Access | Times Cited: 561
Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?
Stefano A. Bini
The Journal of Arthroplasty (2018) Vol. 33, Iss. 8, pp. 2358-2361
Closed Access | Times Cited: 549
Artificial intelligence in COVID-19 drug repurposing
Yadi Zhou, Fei Wang, Jian Tang, et al.
The Lancet Digital Health (2020) Vol. 2, Iss. 12, pp. e667-e676
Open Access | Times Cited: 546
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, et al.
Molecular Pharmaceutics (2017) Vol. 14, Iss. 9, pp. 3098-3104
Closed Access | Times Cited: 531
Exploiting machine learning for end-to-end drug discovery and development
Sean Ekins, Ana C. Puhl, Kimberley M. Zorn, et al.
Nature Materials (2019) Vol. 18, Iss. 5, pp. 435-441
Open Access | Times Cited: 471
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
Ahmet Süreyya Rifaioğlu, Heval Ataş, María Martín, et al.
Briefings in Bioinformatics (2018) Vol. 20, Iss. 5, pp. 1878-1912
Open Access | Times Cited: 445
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Andreas S. Panayides, Amir A. Amini, Nenad Filipović, et al.
IEEE Journal of Biomedical and Health Informatics (2020) Vol. 24, Iss. 7, pp. 1837-1857
Open Access | Times Cited: 421
Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare
Polina Mamoshina, Lucy O. Ojomoko, Yury Yanovich, et al.
Oncotarget (2017) Vol. 9, Iss. 5, pp. 5665-5690
Open Access | Times Cited: 413
Reinforced Adversarial Neural Computer for de Novo Molecular Design
Evgeny Putin, Arip Asadulaev, Yan A. Ivanenkov, et al.
Journal of Chemical Information and Modeling (2018) Vol. 58, Iss. 6, pp. 1194-1204
Open Access | Times Cited: 336
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
Artur Kadurin, Alexander Aliper, Andrey Kazennov, et al.
Oncotarget (2016) Vol. 8, Iss. 7, pp. 10883-10890
Open Access | Times Cited: 335
Machine learning approaches to drug response prediction: challenges and recent progress
George Alexandru Adam, Ladislav Rampášek, Zhaleh Safikhani, et al.
npj Precision Oncology (2020) Vol. 4, Iss. 1
Open Access | Times Cited: 331
De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
Oscar Méndez‐Lucio, Benoît Baillif, Djork-Arné Clevert, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 320
A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions
Tamer N. Jarada, Jon Rokne, Reda Alhajj
Journal of Cheminformatics (2020) Vol. 12, Iss. 1
Open Access | Times Cited: 309
Deep learning in drug discovery: opportunities, challenges and future prospects
Antonio Lavecchia
Drug Discovery Today (2019) Vol. 24, Iss. 10, pp. 2017-2032
Closed Access | Times Cited: 290
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
Alexios Koutsoukas, Keith J. Monaghan, Xiaoli Li, et al.
Journal of Cheminformatics (2017) Vol. 9, Iss. 1
Open Access | Times Cited: 288
Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature
Yoosup Chang, Hyejin Park, Hyun‐Jin Yang, et al.
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 286
A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data
Dongdong Sun, Minghui Wang, Ao Li
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018) Vol. 16, Iss. 3, pp. 841-850
Closed Access | Times Cited: 275
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: 272
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