Drug Release Nanoparticle Systems Design:Dataset Compilation and Machine Learning Modeling

Magnetic Nanoparticles (MNPs) are gaining significant interest in the field of biomedical functional nanomaterials because of their distinctive chemical and physical characteristics, particularly in drug delivery and magnetic hyperthermia applications. In this paper, we experimentally synthesized and characterized new Fe3O4 based MNPs, functionalizing its surface with a 5-TAMRA cadaverine modified copolymer consisting of PMAO and PEG. Despite these advancements, many combinations of NP cores and coatings remain unexplored. To address this, we created a new dataset of MNP systems from public sources. Herein 11 different AI/ML algorithms were used to develop the predictive AI/ML models. The Linear Discriminant Analysis (LDA) and Random Forest (RF) models showed high values of sensitivity and specificity (>0.9) in training/validation series and 3-fold cross validation, respectively. The AI/ML models are able to predict 14 output properties (CC50 (µM), EC50 (µM), Inhibition (%), etc.) for all combinations of 54 different NP cores classes vs. 15 different coats and vs. 41 different cell lines allowing to short list the best results for experimental assays. The results of this work may help to reduce the cost of the traditional trial and error procedures.