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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:

Structure-based de novo drug design using 3D deep generative models
Yibo Li, Jianfeng Pei, Luhua Lai
Chemical Science (2021) Vol. 12, Iss. 41, pp. 13664-13675
Open Access | Times Cited: 105

Showing 1-25 of 105 citing articles:

Deep generative molecular design reshapes drug discovery
Xiangxiang Zeng, Fei Wang, Yuan Luo, et al.
Cell Reports Medicine (2022) Vol. 3, Iss. 12, pp. 100794-100794
Open Access | Times Cited: 122
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu, et al.
Neural Networks (2024) Vol. 173, pp. 106207-106207
Open Access | Times Cited: 108
Structure-based drug design with geometric deep learning
Clemens Isert, Kenneth Atz, Gisbert Schneider
Current Opinion in Structural Biology (2023) Vol. 79, pp. 102548-102548
Open Access | Times Cited: 91
Generative machine learning for de novo drug discovery: A systematic review
Dominic D. Martinelli
Computers in Biology and Medicine (2022) Vol. 145, pp. 105403-105403
Closed Access | Times Cited: 82
Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery
Ri Han, Hongryul Yoon, Gahee Kim, et al.
Pharmaceuticals (2023) Vol. 16, Iss. 9, pp. 1259-1259
Open Access | Times Cited: 60
Is Target-Based Drug Discovery Efficient? Discovery and “Off-Target” Mechanisms of All Drugs
Arash Sadri
Journal of Medicinal Chemistry (2023) Vol. 66, Iss. 18, pp. 12651-12677
Closed Access | Times Cited: 57
High-throughput property-driven generative design of functional organic molecules
Julia Westermayr, Joe Gilkes, Rhyan Barrett, et al.
Nature Computational Science (2023) Vol. 3, Iss. 2, pp. 139-148
Open Access | Times Cited: 46
Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations
Siva Sai, Aanchal Gaur, Revant Sai, et al.
IEEE Access (2024) Vol. 12, pp. 31078-31106
Open Access | Times Cited: 39
Integrating structure-based approaches in generative molecular design
Morgan Thomas, Andreas Bender, Chris de Graaf
Current Opinion in Structural Biology (2023) Vol. 79, pp. 102559-102559
Open Access | Times Cited: 38
A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets
Lei Huang, Tingyang Xu, Yang Yu, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 33
Recent Advances in Automated Structure-Based De Novo Drug Design
Yidan Tang, Rocco Moretti, Jens Meiler
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 6, pp. 1794-1805
Open Access | Times Cited: 20
Generation of 3D molecules in pockets via a language model
Wei Feng, Lvwei Wang, Zaiyun Lin, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 1, pp. 62-73
Open Access | Times Cited: 19
PocketFlow is a data-and-knowledge-driven structure-based molecular generative model
Yuanyuan Jiang, Guo Zhang, Jing You, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 3, pp. 326-337
Open Access | Times Cited: 19
Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides
Montserrat Goles, Anamaria Sanchez–Daza, Gabriel Cabas-Mora, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 15
Invalid SMILES are beneficial rather than detrimental to chemical language models
Michael A. Skinnider
Nature Machine Intelligence (2024) Vol. 6, Iss. 4, pp. 437-448
Open Access | Times Cited: 14
Application of Computational Biology and Artificial Intelligence in Drug Design
Yue Zhang, Mengqi Luo, Peng Wu, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 21, pp. 13568-13568
Open Access | Times Cited: 63
RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
Mingyang Wang, Chang‐Yu Hsieh, Jike Wang, et al.
Journal of Medicinal Chemistry (2022) Vol. 65, Iss. 13, pp. 9478-9492
Closed Access | Times Cited: 58
Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models
Weixin Xie, Fanhao Wang, Yibo Li, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 10, pp. 2269-2279
Closed Access | Times Cited: 47
Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor
Yueshan Li, Liting Zhang, Yifei Wang, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 46
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
A pharmacophore-guided deep learning approach for bioactive molecular generation
Huimin Zhu, Renyi Zhou, Dongsheng Cao, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 23
Deep generative models for 3D molecular structure
Benoît Baillif, Jason C. Cole, Patrick McCabe, et al.
Current Opinion in Structural Biology (2023) Vol. 80, pp. 102566-102566
Open Access | Times Cited: 20
The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery
Thi Nguyen, Diep Thi Ngoc Nguyen, Huan Yee Koh, et al.
British Journal of Pharmacology (2023) Vol. 181, Iss. 14, pp. 2371-2384
Open Access | Times Cited: 20
MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning
Chengwei Ai, Hongpeng Yang, Xiaoyi Liu, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 6, pp. e1012229-e1012229
Open Access | Times Cited: 7
TopMT-GAN: A 3D Topology-Driven Generative Model for Efficient and Diverse Structure-based Ligand Design
Shen Wang, Tong Lin, T. Y. Peng, et al.
Chemical Science (2025)
Open Access
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