The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.

The colonization of microorganisms planted on the surface of teeth and restoration materials is the main cause of oral disease and treatment failure. How to improve the antibacterial properties of dental materials is a hot topic in dentistry. Nano-sized antibacterial materials have attracted much attention. Among them, metal and metal oxide nanoparticles are prominent due to their strong and broad-spectrum antibacterial activity. Thus, in recent years, many studies have used metal and metal oxide nanoparticles to develop antimicrobial dental materials for resin restoration, root canal therapy, orthodontic treatment, and implant surface and removable denture repair and have found that the antibacterial properties of nano-sized materials are significantly enhanced. However, the mechanical properties and esthetic properties of the modified materials are affected, so it is still necessary to explore appropriate modification methods. In addition, most of the experiments are carried out in vitro, which cannot accurately simulate the oral environment. Therefore, the antibacterial effect, cytotoxicity and immune response of these materials in vivo still need further research and exploration. This paper reviewed the potential antibacterial mechanisms and the safety of those nanoparticles and their applications in dentistry.