The development of breast cancer is closely linked to the estrogen receptor ERα, which is also considered to be an important target for the treatment of breast cancer. Therefore, compounds that can antagonize ERα activity may be drug candidates for the treatment of breast cancer. In drug development, to save manpower and resources, potential active compounds are often screened by establishing compound activity prediction model. For the 1974 compounds collected, the top 20 molecular descriptors that significantly affected the biological activity were screened using LASSO regression models combined with 10-fold cross-validation method. Further, a regression prediction model based on the MLP fully connected neural network was constructed to predict the bioactivity values of 50 new compounds. To measure the validity of the model, the model loss term was specified as the mean squared error (MSE). The results showed that the MLP-based regression prediction model had a loss value of 0.0146 on the validation set. This model is therefore well trained and the prediction strategy used is valid. The methods developed by this paper may provide a reference for the development of anti-breast cancer drugs.

SummaryThe development of the internet has brought great convenience to people's travel and shopping. More and more people choose to shop online. As e‐commerce continues to grow in scale, the number and variety of products are also growing rapidly, which results in customers taking a lot of time to find the products they want to buy. This problem prevents people from using the Internet quickly and efficiently. In order to solve these problems, personalized recommendation system comes into being. It can directly predict the content that users may be interested in based on their historical behavior, and make personalized recommendations for them in the massive data. Based on the idea of collaborative filtering, this paper adopts matrix factorization method to analyze the sales records of an e‐commerce platform, and analyzes the potential preferences of 686 customers, and gives the top five personalized recommended products StockCode of users.