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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:

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps
Benjamin P. Brown, Jeffrey Mendenhall, Alexander R. Geanes, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 2, pp. 603-620
Open Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries
Chandrabose Selvaraj, Ishwar Chandra, Sanjeev Kumar Singh
Molecular Diversity (2021) Vol. 26, Iss. 3, pp. 1893-1913
Open Access | Times Cited: 126
Advances in Artificial Intelligence (AI)-assisted approaches in drug screening
Samvedna Singh, Himanshi Gupta, Priyanshu Sharma, et al.
Artificial Intelligence Chemistry (2023) Vol. 2, Iss. 1, pp. 100039-100039
Open Access | Times Cited: 42
The (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods
Thereza A. Soares, Ariane Nunes‐Alves, Angelica Mazzolari, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 22, pp. 5317-5320
Closed Access | Times Cited: 63
Physics-informed neural network for bending and free vibration analysis of three-dimensional functionally graded porous beam resting on elastic foundation
Ali Fallah, M.M. Aghdam
Engineering With Computers (2023)
Closed Access | Times Cited: 38
Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products
Christine Mae F. Ancajas, Abiodun S. Oyedele, Caitlin M. Butt, et al.
Natural Product Reports (2024) Vol. 41, Iss. 10, pp. 1543-1578
Open Access | Times Cited: 9
True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better
Viet‐Khoa Tran‐Nguyen, Guillaume Bret, Didier Rognan
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 6, pp. 2788-2797
Open Access | Times Cited: 45
Structure-based molecular modeling in SAR analysis and lead optimization
Veronika Temml, Zsófia Kutil
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 1431-1444
Open Access | Times Cited: 43
Towards Structure-Guided Development of Pain Therapeutics Targeting Voltage-Gated Sodium Channels
Phuong T. Nguyen, Vladimir Yarov‐Yarovoy
Frontiers in Pharmacology (2022) Vol. 13
Open Access | Times Cited: 32
Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development
Chia‐Ju Hsieh, Sam Giannakoulias, E. James Petersson, et al.
Pharmaceuticals (2023) Vol. 16, Iss. 2, pp. 317-317
Open Access | Times Cited: 19
Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
Tobias Harren, Torben Gutermuth, Christoph Grebner, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 3
Closed Access | Times Cited: 6
Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method
Lina Dong, Xiaoyang Qu, Yuan Zhao, et al.
ACS Omega (2021) Vol. 6, Iss. 48, pp. 32938-32947
Open Access | Times Cited: 36
A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios
Andreas Tosstorff, M.G. Rudolph, Jason C. Cole, et al.
Journal of Computer-Aided Molecular Design (2022) Vol. 36, Iss. 10, pp. 753-765
Closed Access | Times Cited: 24
Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science
Ifra Saifi, Basharat Ahmad Bhat, Syed Suhail Hamdani, et al.
Journal of Biomolecular Structure and Dynamics (2023) Vol. 42, Iss. 12, pp. 6523-6541
Closed Access | Times Cited: 11
The role of artificial intelligence in drug screening, drug design, and clinical trials
Yaojiong Wu, Li Ma, Xinyi Li, et al.
Frontiers in Pharmacology (2024) Vol. 15
Open Access | Times Cited: 4
From data to cures: Leveraging machine learning, deep learning and pharmacore modelling for targeted therapies
Komal Sharma, Vivek Srivastava, Ravi Kant Singh
AIP conference proceedings (2025) Vol. 3262, pp. 020008-020008
Closed Access
QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent
Abduljelil Ajala, Adamu Uzairu, Gideon Adamu Shallangwa, et al.
Future Journal of Pharmaceutical Sciences (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 10
PrePCI: A structure‐ and chemical similarity‐informed database of predicted protein compound interactions
Stephen J. Trudeau, Howook Hwang, Deepika Mathur, et al.
Protein Science (2023) Vol. 32, Iss. 4
Open Access | Times Cited: 9
Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery
Benjamin P. Brown, Oanh Vu, Alexander R. Geanes, et al.
Frontiers in Pharmacology (2022) Vol. 13
Open Access | Times Cited: 15
Structural Comparative Modeling of Multi-Domain F508del CFTR
Eli Fritz McDonald, Hope Woods, Shannon T. Smith, et al.
Biomolecules (2022) Vol. 12, Iss. 3, pp. 471-471
Open Access | Times Cited: 15
An overview of recent advances and challenges in predicting compound-protein interaction (CPI)
Yanbei Li, Zhehuan Fan, Jingxin Rao, et al.
Medical Review (2023) Vol. 3, Iss. 6, pp. 465-486
Open Access | Times Cited: 8
An ensemble‐based approach to estimate confidence of predicted protein–ligand binding affinity values
Milad Rayka, Morteza Mirzaei, Ali Mohammad Latifi
Molecular Informatics (2024) Vol. 43, Iss. 4
Closed Access | Times Cited: 2
Physics-Informed Neural Network for Solution of Nonlinear Differential Equations
Ali Fallah, M.M. Aghdam
(2024), pp. 163-178
Closed Access | Times Cited: 2
DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein–Ligand Binding Affinity Prediction
Jiaqi Liu, Jian Wan, Yanliang Ren, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 15, pp. 4850-4863
Closed Access | Times Cited: 6
AI in 3D compound design
Thomas E. Hadfield, Charlotte M. Deane
Current Opinion in Structural Biology (2022) Vol. 73, pp. 102326-102326
Closed Access | Times Cited: 9
Predicting the functional impact of KCNQ1 variants with artificial neural networks
Saksham Phul, Georg Kuenze, Carlos G. Vanoye, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 4, pp. e1010038-e1010038
Open Access | Times Cited: 8
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