Nashrieh Shimi va Mohandesi Shimi Iran

Nashrieh Shimi va Mohandesi Shimi Iran

Metal-Organic Frameworks and Their Application in Anticancer Drugs: A Machine Learning Approach

Document Type : Research Article

Authors
Minoosh Lalinia 1
Nahid Hassanzadeh Nemati 1
javad karimi-sabet 2
Sayed Khatiboleslam Sadrnezhaad 3
1 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, I.R. IRAN
2 Nuclear Science and Technology Research Institute (NSTRI), Tehran, I.R. IRAN
3 Department of Materials Science and Engineering, Sharif University of Technology, Tehran, I.R. IRAN
Abstract
In this study, Metal-Organic Frameworks (MOFs) were examined as drug carriers due to their porous structures and high loading capacities. The goal of the research was to utilize machine learning algorithms to accurately predict the drug release percentage from MOFs under various conditions. For this purpose, experimental data and scientific literature, including features such as pore size, surface area, density, pore volume, pH, drug loading, time, hydrophobicity, drug type, and MOF type, were used as inputs for machine learning models. The drug release percentage was considered as the output. Experimental data were obtained using various analyses such as SEM, XRD, and BET. Four different algorithms, including Support Vector Regression (SVR), Gradient Boosting, Random Forest, and Decision Tree, were employed to predict drug release percentages.The results showed that the Gradient Boosting algorithm achieved the best performance with R² = 0.85, while Random Forest and Decision Tree provided acceptable results with R² = 0.81 and R² = 0.72, respectively. The SVR model also achieved R² = 0.64. Finally, feature importance analysis revealed that pore size was the most important factor in determining drug release efficiency, with MOF surface area also playing a significant role. These findings demonstrate the high potential of machine learning algorithms in predicting drug release behavior and optimizing MOF design for drug delivery applications.
Keywords
Metal Organic Frameworks(MOFs)
Machine learning
Predict drug release
Feature importance

Subjects

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