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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:

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

Showing 22 citing articles:

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials
Gregory J. O. Beran
Chemical Science (2023) Vol. 14, Iss. 46, pp. 13290-13312
Open Access | Times Cited: 34
Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities
Jeffrey G. Ethier, Evan R. Antoniuk, Blair Brettmann
Soft Matter (2024) Vol. 20, Iss. 29, pp. 5652-5669
Open Access | Times Cited: 6
Hybrid Semi-mechanistic and Machine Learning Solubility Regression Modeling for Crystallization Process Development
Gustavo Lunardon Quilló, Satyajeet Bhonsale, A. Collas, et al.
Crystal Growth & Design (2025)
Closed Access
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
Exploration of the Solubility Hyperspace of Selected Active Pharmaceutical Ingredients in Choline- and Betaine-Based Deep Eutectic Solvents: Machine Learning Modeling and Experimental Validation
Piotr Cysewski, Tomasz Jeliński, Maciej Przybyłek
Molecules (2024) Vol. 29, Iss. 20, pp. 4894-4894
Open Access | Times Cited: 4
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
Developing a model-driven workflow for the digital design of small-scale batch cooling crystallisation with the antiviral lamivudine
Thomas Pickles, Chantal L. Mustoe, Christopher Boyle, et al.
CrystEngComm (2024) Vol. 26, Iss. 6, pp. 822-834
Open Access | Times Cited: 2
Using natural language processing (NLP)-inspired molecular embedding approach to predict Hansen solubility parameters
Jiayun Pang, Alexander W. R. Pine, Abdulai Sulemana
Digital Discovery (2023) Vol. 3, Iss. 1, pp. 145-154
Open Access | Times Cited: 6
Digital process design to define and deliver pharmaceutical particle attributes
Stephanie J. Urwin, Magdalene W. S. Chong, Wei Li, et al.
Process Safety and Environmental Protection (2023) Vol. 196, pp. 726-749
Open Access | Times Cited: 5
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
Optimization and analysis of ultrasound-assisted solvent extraction of bioactive compounds from Boerhavia diffusa Linn. using RSM, ANFIS and machine learning algorithm
Shanmugampillai Jeyarajaguru Kabilan, Oviya Sivakumar, Gorla Buchayyagari Sumanth, et al.
Journal of Food Measurement & Characterization (2024) Vol. 18, Iss. 6, pp. 4204-4220
Closed Access | Times Cited: 1
Prediction of acetylene solubility by a mechanism-data hybrid-driven machine learning model constructed based on COSMO-RS theory
Yao Mu, Tianying Dai, Jiahe Fan, et al.
Journal of Molecular Liquids (2024) Vol. 414, pp. 126194-126194
Closed Access | Times Cited: 1
Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning
Zeqing Bao, Gary Tom, Austin Cheng, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 1
Experiment-based Abraham model solute descriptors for 2,3-dimethoxybenzoic acid from measured solubility data
Jilawan Francis, Ramya Motati, Trisha Kandi, et al.
Physics and Chemistry of Liquids (2024) Vol. 62, Iss. 4, pp. 343-355
Closed Access
Towards the Prediction of Drug Solubility in Binary Solvent Mixtures at Various Temperatures Using Machine Learning
Zeqing Bao, Gary Tom, Austin Cheng, et al.
Research Square (Research Square) (2024)
Open Access
Harnessing AI for Optimizing Formulation Components in Advanced Drug Delivery Systems: Analysis of Large-Scale Screening Data
Yogesh Chaudhari
SSRN Electronic Journal (2024)
Closed Access
A hybrid approach to aqueous solubility prediction using COSMO-RS and machine learning
Niamh Mac Fhionnlaoich, Jacek Żegliński, Melba Simon, et al.
Process Safety and Environmental Protection (2024) Vol. 209, pp. 67-71
Closed Access
Solvmate – a hybrid physical/ML approach to solvent recommendation leveraging a rank-based problem framework
Jan M. Wollschläger, Floriane Montanari
Digital Discovery (2024) Vol. 3, Iss. 9, pp. 1749-1760
Open Access
Corrigendum to “Modeling PROTAC degradation activity with machine learning” [Artif. Intell. Life Sci. 6 (2024) 100104]
Stefano Ribes, Eva Nittinger, Christian Tyrchan, et al.
Artificial Intelligence in the Life Sciences (2024) Vol. 6, pp. 100114-100114
Open Access
Enabling Data-Driven Solubility Modeling at GSK: Enhancing Purge Predictions for Mutagenic Impurities
Luigi Da Vià, Matthias Depoortere, Robert D. Willacy, et al.
Organic Process Research & Development (2024)
Closed Access
Extreme Gradient Boosting Combined with Conformal Predictors for Informative Solubility Estimation
Ozren Jović, Rabah Mouras
Molecules (2023) Vol. 29, Iss. 1, pp. 19-19
Open Access | Times Cited: 1
Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks
Yeonjoon Kim, Hojin Jung, Sabari Kumar, et al.
(2023)
Open Access
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