Research graph
References from Comparative analysis using machine learning methods for determination of pharmaceutical solubility in supercritical CO2 considering physical properties of drugs. Local targets link to admitted publications; unresolved targets remain external evidence.
A comprehensive overview of the role of intermolecular interactions in amorphous solid dispersions
10.1016/j.ijpharm.2025.125441 · 2025 · External reference
Advancing amorphous solid dispersions design: insights into dissolution kinetics via thermodynamic descriptor and machine learning
10.1021/acs.molpharmaceut.5c00518 · 2025 · External reference
Advances in formulation strategies and stability considerations of amorphous solid dispersions
10.1016/j.jddst.2025.106922 · 2025 · External reference
Supercritical carbon dioxide-based technologies for the production of drug nanoparticles/nanocrystals – A comprehensive review
10.1016/j.addr.2018.07.010 · 2018 · External reference
Nanoparticles and nanocrystals by supercritical CO2-assisted techniques for pharmaceutical applications: a review
10.3390/app11041476 · 2021 · External reference
Thermodynamic modeling of anticancer drugs solubilities in supercritical CO2 using the PC-SAFT equation of state
10.1016/j.fluid.2024.114202 · 2025 · External reference
Experimental measurement and thermodynamic modeling of solubility of Riluzole drug (neuroprotective agent) in supercritical carbon dioxide
10.1016/j.fluid.2022.113711 · 2023 · External reference
Determination of regorafenib monohydrate (colorectal anticancer drug) solubility in supercritical CO2: experimental and thermodynamic modeling
10.1016/j.heliyon.2024.e29049 · 2024 · External reference
Solubility modeling of Levofloxactin hemihydrate in supercritical CO2 green solvent using Soave-Redlich-Kwong EoS and density-based models
10.1016/j.jtice.2025.106156 · 2025 · External reference
Determination of gefitinib hydrochloride anti-cancer drug solubility in supercritical CO2: evaluation of sPC-SAFT EoS and semi-empirical models
10.1016/j.jtice.2024.105569 · 2024 · External reference
Application of machine learning approach to estimate the solubility of some solid drugs in supercritical CO2
10.1038/s41598-025-89858-5 · 2025 · External reference
Machine learning based modeling for estimation of drug solubility in supercritical fluid by adjusting important parameters
10.1016/j.chemolab.2024.105241 · 2024 · External reference
Machine learning analysis of drug solubility via green approach to enhance drug solubility for poor soluble medications in continuous manufacturing
10.1038/s41598-025-11823-z · 2025 · External reference
Artificial intelligence optimization of alendronate solubility in CO2 supercritical system: computational modeling and predictive simulation
10.1016/j.asej.2024.102905 · 2024 · External reference
Intelligence modeling of pharmaceutical solubility in supercritical CO2: polynomial regression, extra trees, and huber regression models and validation
10.1016/j.cjph.2025.07.037 · 2025 · External reference
Machine learning-based prediction and mathematical optimization of Capecitabine solubility through the supercritical CO2 system
10.1016/j.molliq.2023.123229 · 2023 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Comparison of min-max normalization and Z-score normalization in the K-nearest neighbor (kNN) algorithm to test the accuracy of types of breast cancer
10.47738/ijiis.v4i1.73 · 2021 · External reference
An overview of statistical learning theory
10.1109/72.788640 · 1999 · External reference
10.1017/cbo9780511809682
10.1017/cbo9780511809682 · 2004 · External reference
Unresolved reference
2011 · External reference
10.1007/3-540-44581-1_27
10.1007/3-540-44581-1_27 · 2001 · External reference
Lightgbm: a highly efficient gradient boosting decision tree
2017 · External reference
Greedy function approximation: a gradient boosting machine
10.1214/aos/1013203451 · 2001 · External reference
10.1007/978-1-4614-7138-7
10.1007/978-1-4614-7138-7 · 2013 · External reference
Neural additive models: interpretable machine learning with neural nets
2021 · External reference
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
10.1038/s42256-019-0048-x · 2019 · External reference
Hyperband: a novel bandit-based approach to hyperparameter optimization
2018 · External reference
Unresolved reference
2016 · External reference
