Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning

Metal-organic frameworks (MOFs) have been widely researched as drug delivery systems due to their intrinsic porous structures. Herein, machine learning (ML) technologies were applied for the screening of MOFs with high drug loading capacity. To achieve this, first, a comprehensive dataset was gathered, including 40 data points from more than 100 different publications. The organic linkers, metal ions, and the functional groups, as well as the surface area and the pore volume of the investigated MOFs, were chosen as the model’s inputs, and the output was the ibuprofen (IBU) loading capacity. Thereafter, various advanced and powerful machine learning algorithms, such as support vector regression (SVR), random forest (RF), adaptive boosting (AdaBoost), and categorical boosting (CatBoost), were employed to predict the ibuprofen loading capacity of MOFs. The coefficient of determination (R2) of 0.70, 0.72, 0.66, and 0.76 were obtained for the SVR, RF, AdaBoost, and CatBoost approaches, respectively. Among all the algorithms, CatBoost was the most reliable, exhibiting superior performance regarding the sparse matrices and categorical features. Shapley additive explanations (SHAP) analysis was employed to explore the impact of the eigenvalues of the model’s outputs. Our initial results indicate that this methodology is a well generalized, straightforward, and cost-effective method that can be applied not only for the prediction of IBU loading capacity, but also in many other biomaterials projects

Antibacterial Effects of a Cell-Penetrating Peptide Isolated from Kefir

Kefir is a traditional fermented milk beverage used throughout the world for centuries. A cell-penetrating peptide, F3, was isolated from kefir by Sephadex G-50 gel filtration, DEAE-52 ion exchange, and reverse-phase high-performance liquid chromatography. F3 was determined to be a low molecular weight peptide containing one leucine and one tyrosine with two phosphate radicals. This peptide displayed antimicrobial activity across a broad spectrum of organisms including several Gram-positive and Gram-negative bacteria as well as fungi, with minimal inhibitory concentration (MIC) values ranging from 125 to 500 μg/mL. Cellular penetration and accumulation of F3 were determined by confocal laser scanning microscopy. The peptide was able to penetrate the cellular membrane of Escherichia coli and Staphylococcus aureus. Changes in cell morphology were observed by scanning electron microscopy (SEM). The results indicate that peptide F3 may be a good candidate for use as an effective biological preservative in agriculture and the food industry