Abstract
In order to improve the efficiency and accuracy of drug design, this study explored the algorithm of drug molecular structure optimization and activity prediction based on quantum computing and deep learning. Firstly, the paper constructs a comprehensive algorithm framework, which combines quantum computing simulation with deep learning model to realize the integration of structural optimization and activity prediction of drug molecules. Secondly, in this study, a variety of molecular data sets were constructed by using the open drug activity database, and were verified by experiments. The results show that our proposed algorithm has high accuracy and efficiency in the activity prediction task, which is superior to the traditional drug design method. In addition, we deeply analyzed and explained the predicted results of the model, which provided important theoretical guidance for drug design. Finally, the application prospect of this algorithm in the field of drug design is prospected, and it is expected to provide important technical support for the discovery and development of new drugs. To sum up, this study provides new ideas and methods for the progress and innovation in the field of drug design.