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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:

Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values
Raquel Rodríguez-Pérez, Jürgen Bajorath
Journal of Medicinal Chemistry (2019) Vol. 63, Iss. 16, pp. 8761-8777
Open Access | Times Cited: 319

Showing 1-25 of 319 citing articles:

Drug discovery with explainable artificial intelligence
José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider
Nature Machine Intelligence (2020) Vol. 2, Iss. 10, pp. 573-584
Open Access | Times Cited: 709
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
Raquel Rodríguez-Pérez, Jürgen Bajorath
Journal of Computer-Aided Molecular Design (2020) Vol. 34, Iss. 10, pp. 1013-1026
Open Access | Times Cited: 436
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Dejun Jiang, Zhenhua Wu, Chang‐Yu Hsieh, et al.
Journal of Cheminformatics (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 398
Advanced machine-learning techniques in drug discovery
Moe Elbadawi, Simon Gaisford, Abdul W. Basit
Drug Discovery Today (2020) Vol. 26, Iss. 3, pp. 769-777
Open Access | Times Cited: 138
Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review
Rocco Meli, Garrett M. Morris, Philip C. Biggin
Frontiers in Bioinformatics (2022) Vol. 2
Open Access | Times Cited: 76
A Perspective on Explanations of Molecular Prediction Models
Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 8, pp. 2149-2160
Open Access | Times Cited: 47
ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data
Arkaprava Banerjee, Kunal Roy
Environmental Science Processes & Impacts (2024) Vol. 26, Iss. 6, pp. 991-1007
Open Access | Times Cited: 25
Advancing material property prediction: using physics-informed machine learning models for viscosity
Alex K. Chew, Matthew Sender, Zachary Kaplan, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 20
Machine‐learning scoring functions for structure‐based drug lead optimization
Hongjian Li, Kam‐Heung Sze, Gang Lü, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2020) Vol. 10, Iss. 5
Open Access | Times Cited: 129
Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship
Maciej Staszak, Katarzyna Staszak, Karolina Wieszczycka, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2021) Vol. 12, Iss. 2
Open Access | Times Cited: 89
Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms
Mengwei Ma, Renyi Liu, Chanjuan Wen, et al.
European Radiology (2021) Vol. 32, Iss. 3, pp. 1652-1662
Closed Access | Times Cited: 74
Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment
José Jiménez-Luna, Miha Škalič, Nils Weskamp, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 3, pp. 1083-1094
Open Access | Times Cited: 65
Explainable Machine Learning for Property Predictions in Compound Optimization
Raquel Rodríguez-Pérez, Jürgen Bajorath
Journal of Medicinal Chemistry (2021) Vol. 64, Iss. 24, pp. 17744-17752
Closed Access | Times Cited: 60
Quantitative evaluation of explainable graph neural networks for molecular property prediction
Jiahua Rao, Shuangjia Zheng, Yutong Lu, et al.
Patterns (2022) Vol. 3, Iss. 12, pp. 100628-100628
Open Access | Times Cited: 46
Interpretation of Structure–Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence
Tobias Harren, Hans Matter, Gerhard Heßler, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 3, pp. 447-462
Closed Access | Times Cited: 45
Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models
Tengyi Zhu, Yu Zhang, Cuicui Tao, et al.
The Science of The Total Environment (2022) Vol. 857, pp. 159348-159348
Closed Access | Times Cited: 44
Risk factors for severe COVID-19 differ by age for hospitalized adults
Sevda Molani, Patricia V. Hernandez, Ryan Roper, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 43
The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study
Yixin Wang, Jinwei Lang, Joey Zhaoyu Zuo, et al.
European Radiology (2022) Vol. 32, Iss. 12, pp. 8737-8747
Closed Access | Times Cited: 42
An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice
Wei-Chun Chou, Qiran Chen, Long Yuan, et al.
Journal of Controlled Release (2023) Vol. 361, pp. 53-63
Open Access | Times Cited: 40
Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification
Olatomiwa O. Bifarin
PLoS ONE (2023) Vol. 18, Iss. 5, pp. e0284315-e0284315
Open Access | Times Cited: 36
Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models
Joseph D. Janizek, Ayse B. Dincer, Safiye Çelik, et al.
Nature Biomedical Engineering (2023) Vol. 7, Iss. 6, pp. 811-829
Closed Access | Times Cited: 28
Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models
Adem R.N. Aouichaoui, Fan Fan, Seyed Soheil Mansouri, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 3, pp. 725-744
Open Access | Times Cited: 27
Explainability and white box in drug discovery
Kevser Kübra Kırboğa, Sumra Wajid Abbasi, Ecir Uğur Küçüksille
Chemical Biology & Drug Design (2023) Vol. 102, Iss. 1, pp. 217-233
Closed Access | Times Cited: 26
Diagnosis of autism spectrum disorder based on functional brain networks and machine learning
Caroline L. Alves, Thaise G. L. de O. Toutain, Patrícia de Carvalho Aguiar, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 24
Towards white box modeling of compressive strength of sustainable ternary cement concrete using explainable artificial intelligence (XAI)
Syed Muhammad Ibrahim, Saad Shamim Ansari, Syed Danish Hasan
Applied Soft Computing (2023) Vol. 149, pp. 110997-110997
Closed Access | Times Cited: 23
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