Integrating optimal prediction model with hybrid deep learning for coated metal oxide nanoparticle drug delivery...

The increasing need for precision therapies, modern medicine depends on the discovery and development of efficient Drug Delivery Nano Systems (DDNS). its special physicochemical characteristics, such as its high stability, biocompatibilities, and potential for targeted distribution, metal oxide nanoparticles (MONP), have become attractive options for drug delivery. On the other hand, finding the medication and MONP coating agent combinations is still very difficult, frequently made by the intricacy of molecular interactions and the dearth of reliable prediction models are more difficult. To improve the design of DDNS, this research presents a unique framework which combines a hybrid deep learning with an optimum prediction model. Our method preprocesses the data from preclinical drug testing and MONP datasets using sophisticated optimization algorithms, such as Aptenodytes Forsteri optimization (AFO) and Deer Hunting Optimization (DHO). The best drug-MONP is a combination of predicted methods using a dynamic duplex deep neural network (D3NN), which is guaranteed to meet the accurate target and enhance the therapeutic effectiveness. The ChEMBL-MONP datasets, which include a variety of molecular and physicochemical characteristics that are used to validate the efficiency of the suggested model. Of comparing the DHO-AFO-D3NN model, the current PTML and PTML-ANN models they prove the significant gains seen in all performance indicators. With a substantial accuracy gain of 3.82 %, the DHO-AFO-D3NN model outperforms the top-performing PTML model, PTML-RF. There is a notable increase to 6.66 % in sensitivity and a 2.52 % improvement in precision. Further, there is a 5.25 % rise in the F-measure, a 2.47 % improvement in specificity, and a 3.88 % improvement in the AUROC score. Similarly, the DHO-AFO-D3NN model continues to perform better than PTML-ANN models. The suggested model outperforms the leading PTML-ANN model, PTML-LNN, by 2.49 % in accuracy, 2.52 % in precision, and 2.68 % in sensitivity. There is a 2.60 % improvement in the F-measure, a 2.45 % rise in specificity, and a 2.44 % improvement in the AUROC score. These findings highlight how well the DHO-AFO-D3NN model predicts the best medication and MONP combinations for DDNS. The notable gains in every parameter demonstrate the model's exceptional capacity to achieve accurate target, lower mistakes, and facilitate the effective design of the sophisticated drug delivery systems, all contribute to the advancement of precision medicine and better therapeutic results.