Background Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks. Methods We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without having input data leakage. We compare our model with several matrix factorization methods and a deep model on three COVID-19 datasets. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Furthermore, as an external validation, we conduct a docking study on the COVID-19 recommended drugs. Results In all cases, the results confirm that DRaW outperforms matrix factorization and deep models. The docking results approve the top-ranked recommended drugs for COVID-19. Conclusions In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on three COVID-19 and four benchmark datasets.

The aim of this study was preparation and optimisation of a controlled-release delivery system to decrease the dosedependent side effects of gentamicin. Hydrogel nanoparticles composed of a polycationic polymer (chitosan) and an inorganic polyanion (sodium tripolyphosphate) were fabricated in the presence of gentamicin. An experimental design was drawn upon to determine the optimum condition of nanoparticle preparation. Various features of the nanoparticles including drug loading parameters, particle size distribution, zeta potential and in vitro drug release profile were evaluated. Ultimately, the antimicrobial activity of the gentamicin-loaded nanoparticles was analysed by determination of the minimum inhibitory concentration (MIC) and the potency test. As a result, the nanocarriers with an average size of about 250 nm (unloaded) and 493 nm (gentamicinloaded) were obtained with unimodal distribution and a notable polydispersity index (≤0.3). The drug loading efficiency was between 28 and 32%. The gradual and sustained releases (∼90%) of gentamicin were achieved in 24 h. The MIC and potency test showed no significant decrease in the antibacterial activity of gentamicin-loaded nanoparticles. The outcomes demonstrated that the optimised chitosan nanogels prepared in this study can be considered as a suitable carrier for a controlled release system.