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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:

Solubility Challenge Revisited after Ten Years, with Multilab Shake-Flask Data, Using Tight (SD ∼ 0.17 log) and Loose (SD ∼ 0.62 log) Test Sets
Antonio Llinàs, Alex Avdeef
Journal of Chemical Information and Modeling (2019) Vol. 59, Iss. 6, pp. 3036-3040
Closed Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
Samuel Boobier, David R. J. Hose, A. John Blacker, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 214
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
Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database
Alex Avdeef
ADMET & DMPK (2020) Vol. 8, Iss. 1, pp. 29-77
Open Access | Times Cited: 51
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
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
Las Vegas algorithm in the prediction of intrinsic solubility of drug-like compounds
Aleksandar M. Veselinović, Alla P. Toropova, Andrey A. Toropov, et al.
Journal of Molecular Graphics and Modelling (2025) Vol. 137, pp. 109004-109004
Closed Access
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
D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU
Diogo Santos‐Martins, Jérôme Eberhardt, Giulia Bianco, et al.
Journal of Computer-Aided Molecular Design (2019) Vol. 33, Iss. 12, pp. 1071-1081
Open Access | Times Cited: 33
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
Different molecular enumeration influences in deep learning: an example using aqueous solubility
Jen‐Hao Chen, Yufeng Jane Tseng
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 25
ADME Prediction with KNIME: In silico aqueous solubility models based on supervised recursive machine learning approaches
Gabriela Falcón-Cano, Christophe Molina, Miguel Ángel Cabrera‐Pérez
ADMET & DMPK (2020)
Open Access | Times Cited: 24
Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics
Daniel J. Fowles, David Scott Palmer, Rui Guo, et al.
Journal of Chemical Theory and Computation (2021) Vol. 17, Iss. 6, pp. 3700-3709
Open Access | Times Cited: 22
Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases
Christophe Müller, Obdulia Rabal, Constantino Diaz Gonzalez
Methods in molecular biology (2021), pp. 383-407
Closed Access | Times Cited: 21
Chemoinformatic regression methods and their applicability domain
Thomas‐Martin Dutschmann, Valerie Schlenker, Knut Baumann
Molecular Informatics (2024) Vol. 43, Iss. 7
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
Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances
Mare Oja, Sulev Sild, Geven Piir, et al.
Pharmaceutics (2022) Vol. 14, Iss. 10, pp. 2248-2248
Open Access | Times Cited: 12
High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research
Yangang Liang, Heather Job, Ruozhu Feng, et al.
Cell Reports Physical Science (2023) Vol. 4, Iss. 10, pp. 101633-101633
Open Access | Times Cited: 7
UNGAP best practice for improving solubility data quality of orally administered drugs
Maria Vertzoni, Jochem Alsenz, Patrick Augustijns, et al.
European Journal of Pharmaceutical Sciences (2021) Vol. 168, pp. 106043-106043
Open Access | Times Cited: 17
Reversible interconversion of pharmaceutical salt polymorphs facilitated by mechanical methods
Liulei Ma, Qixuan Zheng, Daniel K. Unruh, et al.
Chemical Communications (2023) Vol. 59, Iss. 50, pp. 7779-7782
Open Access | Times Cited: 6
Multi-lab intrinsic solubility measurement reproducibility in CheqSol and shake-flask methods
Alex Avdeef
ADMET & DMPK (2019) Vol. 7, Iss. 3, pp. 210-219
Open Access | Times Cited: 19
High-Throughput Virtual Screening of Quinones for Aqueous Redox Flow Batteries: Status and Perspectives
Abhishek Khetan
Batteries (2022) Vol. 9, Iss. 1, pp. 24-24
Open Access | Times Cited: 10
Designing solvent systems using self-evolving solubility databases and graph neural networks
Yeonjoon Kim, Hojin Jung, Sabari Kumar, et al.
Chemical Science (2023) Vol. 15, Iss. 3, pp. 923-939
Open Access | Times Cited: 5
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.
(2020)
Open Access | Times Cited: 14
Predicting small molecules solubility on endpoint devices using deep ensemble neural networks
Mayk Caldas Ramos, Andrew Dickson White
Digital Discovery (2024) Vol. 3, Iss. 4, pp. 786-795
Open Access | Times Cited: 1
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