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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:

DTITR: End-to-end drug–target binding affinity prediction with transformers
Nelson R. C. Monteiro, José Luís Oliveira, Joel P. Arrais
Computers in Biology and Medicine (2022) Vol. 147, pp. 105772-105772
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Attention is all you need: utilizing attention in AI-enabled drug discovery
Yang Zhang, Caiqi Liu, Mujiexin Liu, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 102
TripletMultiDTI: Multimodal representation learning in drug-target interaction prediction with triplet loss function
Alireza Dehghan, Parvin Razzaghi, Karim Abbasi, et al.
Expert Systems with Applications (2023) Vol. 232, pp. 120754-120754
Closed Access | Times Cited: 48
Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
Amit Gangwal, M. Azim Ansari, Iqrar Ahmad, et al.
Frontiers in Pharmacology (2024) Vol. 15
Open Access | Times Cited: 19
Structure‐Based Drug Discovery with Deep Learning**
Rıza Özçelik, Derek van Tilborg, José Jiménez-Luna, et al.
ChemBioChem (2023) Vol. 24, Iss. 13
Open Access | Times Cited: 33
Advancing drug discovery with deep attention neural networks
Antonio Lavecchia
Drug Discovery Today (2024) Vol. 29, Iss. 8, pp. 104067-104067
Open Access | Times Cited: 9
GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion
Youzhi Liu, Linlin Xing, Longbo Zhang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
DO-GMA: An End-to-End Drug–Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism
Lihong Peng, Jiangyang Mao, Guohua Huang, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing
Bomin Wei, Yue Zhang, Xiang Gong
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 32
Crop genomic selection with deep learning and environmental data: A survey
Sheikh Jubair, Michael Domaratzki
Frontiers in Artificial Intelligence (2023) Vol. 5
Open Access | Times Cited: 16
Application of Transformers in Cheminformatics
Kha-Dinh Luong, Ambuj K. Singh
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 11, pp. 4392-4409
Open Access | Times Cited: 5
PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
Tyler Rose, Nicolò Monti, N Koodagi Anand, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3
A review of transformers in drug discovery and beyond
Jian Jiang, Long Chen, Ke Lü, et al.
Journal of Pharmaceutical Analysis (2024), pp. 101081-101081
Open Access | Times Cited: 3
TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers
Nelson R. C. Monteiro, José Luís Oliveira, Joel P. Arrais
Expert Systems with Applications (2023) Vol. 238, pp. 122334-122334
Open Access | Times Cited: 8
A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning
Xin Zeng, Shujuan Li, Shuang‐Qing Lv, et al.
Frontiers in Pharmacology (2024) Vol. 15
Open Access | Times Cited: 2
A review of machine learning-based methods for predicting drug–target interactions
Weisong Shi, Hong Yang, Linhai Xie, et al.
Health Information Science and Systems (2024) Vol. 12, Iss. 1
Closed Access | Times Cited: 2
Fine-Tuning Artificial Neural Networks to Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan
Galiya Anarbekova, L. G. B. Ruiz, Akerke Аkanova, et al.
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 2, pp. 1154-1169
Open Access | Times Cited: 2
Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders
Lingling Zhao, Yan Zhu, Naifeng Wen, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2024) Vol. 21, Iss. 5, pp. 1458-1467
Closed Access | Times Cited: 1
Attention-based approach to predict drug-target interactions across seven target superfamilies
Aron Schulman, Juho Rousu, Tero Aittokallio, et al.
Bioinformatics (2024) Vol. 40, Iss. 8
Open Access | Times Cited: 1
Innovative Mamba and graph transformer framework for superior protein-ligand affinity prediction
Kaitai Han, C. Shi, Zijun Wang, et al.
Microchemical Journal (2024) Vol. 206, pp. 111444-111444
Closed Access | Times Cited: 1
Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review
Jingjing Wang, Chang Chen, Ge Yao, et al.
Molecules (2023) Vol. 28, Iss. 23, pp. 7865-7865
Open Access | Times Cited: 3
Static–Dynamic coordinated Transformer for Tumor Longitudinal Growth Prediction
Hexi Wang, Ning Xiao, Jina Zhang, et al.
Computers in Biology and Medicine (2022) Vol. 148, pp. 105922-105922
Closed Access | Times Cited: 6
Quality Matters: Deep Learning-Based Analysis of Protein-Ligand Interactions with Focus on Avoiding Bias
Manuel Sellner, Markus A. Lill, Martin Smieško
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
XG-DTA: Drug-Target Affinity Prediction Based on Drug Molecular Graph and Protein Sequence combined with XLNet
Han Zhou, Xiumin Shi, Yuxiang Wang, et al.
(2023), pp. 189-196
Closed Access | Times Cited: 2
Attention-based approach to predict drug-target interactions across seven target superfamilies
Aron Schulman, Juho Rousu, Tero Aittokallio, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Opportunities and Challenges of Artificial Intelligence ( AI ) in Drug Delivery
Zhuyifan Ye, Defang Ouyang
(2024), pp. 10-58
Closed Access
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