Open Access Helper

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:

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

Showing 26-50 of 43 citing articles:

MEGAN: Multi-explanation Graph Attention Network
Jonas Teufel, Luca Torresi, Patrick Reiser, et al.
Communications in computer and information science (2023), pp. 338-360
Closed Access | Times Cited: 3
Intrinsic Solubility of Ionizable Compounds from pKa Shift
Joku̅bas Preikša, Vilma Petrikaitė, Vytautas Petrauskas, et al.
ACS Omega (2023) Vol. 8, Iss. 47, pp. 44571-44577
Open Access | Times Cited: 3
Explaining structure-activity relationships using locally faithful surrogate models
Heta A. Gandhi, Andrew Dickson White
(2022)
Open Access | Times Cited: 5
A global model for simulation of drug solubility in mono-solvents at different temperatures
Abolghasem Jouyban
Journal of Molecular Liquids (2023) Vol. 389, pp. 122850-122850
Closed Access | Times Cited: 2
RedPred, a machine learning model for the prediction of redox reaction energies of the aqueous organic electrolytes
Murat Cihan Sorkun, Elham Nour Ghassemi, Cihan Yatbaz, et al.
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 1, pp. 100064-100064
Open Access
Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models
Viktor Drgan, Katja Venko, Janja Sluga, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 8, pp. 4156-4156
Open Access
Structure–Activity Relationship (SAR) Modeling of Mosquito Repellents: Deciphering the Importance of the 1-Octanol/Water Partition Coefficient on the Prediction Results
James Devillers, Hugo Devillers
Applied Sciences (2024) Vol. 14, Iss. 13, pp. 5366-5366
Open Access
Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set
J Q Zhao, Eline Hermans, Kia Sepassi, et al.
Molecular Pharmaceutics (2024)
Open Access
Thermodynamic Assessment of the Pyrazinamide Dissolution Process in Some Organic Solvents
Jesus Tovar-Amézquita, Cristian Rincón-Guio, Francy Elaine Torres-Suarez, et al.
Molecules (2024) Vol. 29, Iss. 21, pp. 5089-5089
Open Access
Advancing energy storage through solubility prediction: leveraging the potential of deep learning
Mesfin Diro Chaka, Yedilfana Setarge Mekonnen, Qin Wu, et al.
Physical Chemistry Chemical Physics (2023) Vol. 25, Iss. 46, pp. 31836-31847
Open Access | Times Cited: 1
Transformer Based Molecule Encoding for Property Prediction
Prateeth Nayak, Andrew Silberfarb, Ran Chen, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 2
Prediction of Aqueous Solubility of Drug Molecules by Embedding Spatial Conformers Using Graph Neural Networks
Mohammad Erfan Hamdi, Rasool Dezhkam, Arman Hajizade, et al.
(2022), pp. 46-50
Closed Access | Times Cited: 1
Predicting the Solubility of Inorganic Ion Pairs in Water
Tasnim Rahman, Enric Petrus, Mireia Segado, et al.
Angewandte Chemie (2022) Vol. 134, Iss. 19
Open Access | Times Cited: 1
Explaining structure-activity relationships using locally faithful surrogate models
Heta A. Gandhi, Andrew Dickson White
(2022)
Open Access | Times Cited: 1
An Evaluation of Neural Network Performance Using Complex-Valued Input Data
Kushal Thapa, Stan McClellan
(2021), pp. 27-31
Closed Access | Times Cited: 1
Machine Learning for FB Electrolyte Screening
Laura‐Sophie Berg, Jan Hamaekers, Astrid Maaß
(2023), pp. 487-506
Closed Access
Data-driven approaches enabling the screening and design of promising materials for energy storage batteries
Qi ZHANG, Chao Peng, Dongfeng Xue
Scientia Sinica Technologica (2023)
Closed Access
Machine Learning for Flow Battery Systems
A.A. Shah, Puiki Leung, Qian Xu, et al.
Engineering Applications of Computational Methods (2023), pp. 175-284
Closed Access
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