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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:

Findings of the Second Challenge to Predict Aqueous Solubility
Antonio Llinàs, Ioana Oprisiu, Alex Avdeef
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 10, pp. 4791-4803
Closed Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

Model agnostic generation of counterfactual explanations for molecules
Geemi P. Wellawatte, Aditi Seshadri, Andrew Dickson White
Chemical Science (2022) Vol. 13, Iss. 13, pp. 3697-3705
Open Access | Times Cited: 94
SolTranNet–A Machine Learning Tool for Fast Aqueous Solubility Prediction
Paul Francoeur, David Ryan Koes
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 6, pp. 2530-2536
Open Access | Times Cited: 61
Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction
Gihan Panapitiya, Michael Girard, Aaron Hollas, et al.
ACS Omega (2022) Vol. 7, Iss. 18, pp. 15695-15710
Open Access | Times Cited: 53
A unified ML framework for solubility prediction across organic solvents
Antony D. Vassileiou, Murray N. Robertson, Bruce G. Wareham, et al.
Digital Discovery (2023) Vol. 2, Iss. 2, pp. 356-367
Open Access | Times Cited: 22
SolPredictor: Predicting Solubility with Residual Gated Graph Neural Network
Waqar Ahmad, Hilal Tayara, Hyun Joo Shim, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 2, pp. 715-715
Open Access | Times Cited: 10
Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation
Muteb Alanazi, Jowaher Alanazi, Tareq Nafea Alharby, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Refined ADME Profiles for ATC Drug Classes
Luca Menestrina, Raquel Parrondo-Pizarro, Ismael Gómez, et al.
Pharmaceutics (2025) Vol. 17, Iss. 3, pp. 308-308
Open Access
Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?
Mario Lovrić, Kristina Pavlović, Petar Žuvela, et al.
Journal of Chemometrics (2021) Vol. 35, Iss. 7-8
Open Access | Times Cited: 51
Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset
Xiangwei Zhu, Valery Polyakov, Krishna Bajjuri, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 10, pp. 2948-2959
Closed Access | Times Cited: 15
Will we ever be able to accurately predict solubility?
Pierre Llompart, Claire Minoletti, Shamkhal Baybekov, et al.
Scientific Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 6
The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge
Andrea Hunklinger, Peter B. R. Hartog, Martin Šícho, et al.
SLAS DISCOVERY (2024) Vol. 29, Iss. 2, pp. 100144-100144
Open Access | Times Cited: 5
Pushing the limits of solubility prediction via quality-oriented data selection
Murat Cihan Sorkun, J. M. V. A. Koelman, Süleyman Er
iScience (2020) Vol. 24, Iss. 1, pp. 101961-101961
Open Access | Times Cited: 43
Predicting Solubility of Newly-Approved Drugs (2016–2020) with a Simple ABSOLV and GSE(Flexible-Acceptor) Consensus Model Outperforming Random Forest Regression
Alex Avdeef, Manfred Kansy
Journal of Solution Chemistry (2022) Vol. 51, Iss. 9, pp. 1020-1055
Open Access | Times Cited: 22
Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
Jonathan G. M. Conn, James W. Carter, Justin J. A. Conn, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 4, pp. 1099-1113
Open Access | Times Cited: 13
Pharmaceutical Digital Design: From Chemical Structure through Crystal Polymorph to Conceptual Crystallization Process
Christopher L. Burcham, Michael F. Doherty, Baron Peters, et al.
Crystal Growth & Design (2024) Vol. 24, Iss. 13, pp. 5417-5438
Open Access | Times Cited: 4
Explaining molecular properties with natural language
Heta A. Gandhi, Andrew Dickson White
(2022)
Open Access | Times Cited: 21
Free Energy Perturbation Approach for Accurate Crystalline Aqueous Solubility Predictions
Richard S. Hong, Ana V. Rojas, Rajni M. Bhardwaj, et al.
Journal of Medicinal Chemistry (2023) Vol. 66, Iss. 23, pp. 15883-15893
Closed Access | Times Cited: 10
A Machine Learning Approach for the Prediction of Aqueous Solubility of Pharmaceuticals: A Comparative Model and Dataset Analysis
Mohammad Amin Ghanavati, Soroush Ahmadi, Sohrab Rohani
Digital Discovery (2024)
Open Access | Times Cited: 3
Lost in modelling and simulation?
Kiyohiko Sugano
ADMET & DMPK (2021) Vol. 9, Iss. 2, pp. 75-109
Open Access | Times Cited: 24
Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules
Elena M. Tosca, Roberta Bartolucci, Paolo Magni
Pharmaceutics (2021) Vol. 13, Iss. 7, pp. 1101-1101
Open Access | Times Cited: 24
Solubility Characteristics of Acetaminophen and Phenacetin in Binary Mixtures of Aqueous Organic Solvents: Experimental and Deep Machine Learning Screening of Green Dissolution Media
Piotr Cysewski, Tomasz Jeliński, Maciej Przybyłek, et al.
Pharmaceutics (2022) Vol. 14, Iss. 12, pp. 2828-2828
Open Access | Times Cited: 15
Pharmaceutical Digital Design: From Chemical Structure through Crystal Polymorph to Conceptual Crystallization Process
Christopher L. Burcham, Michael F. Doherty, Baron Peters, et al.
(2024)
Open Access | Times Cited: 2
Outline and Background for the EU-OS Solubility Prediction Challenge
Wenyu Wang, Jing Tang, Andrea Zaliani
SLAS DISCOVERY (2024) Vol. 29, Iss. 4, pp. 100155-100155
Open Access | Times Cited: 2
Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data
Tonglei Li, Nicholas J. Huls, Shan Lu, et al.
Communications Chemistry (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 2
Graph Neural Networks for Predicting Solubility in Diverse Solvents Using MolMerger Incorporating Solute–Solvent Interactions
Vansh Ramani, Tarak Karmakar
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 15, pp. 6549-6558
Open Access | Times Cited: 2
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