Optimization Method of an Antibreast Cancer Drug Candidate Based on Machine Learning

Breast cancer is a common but serious and even lethal disease. Fortunately, compared with other cancers, breast cancer treatments currently are relatively well developed. The use of specific drugs is typically essential in the majority of breast cancer treatment strategies. Given the aforementioned factors, it is important to continue researching effective antibreast cancer drug design. Machine learning-based computer-aided drug design is currently a common practice in both drug industries and academic institutes. According to the characteristics of breast cancer, we selected multiple candidate compounds; based on the corresponding molecular descriptors, biological activities, and pharmacokinetic properties, a dataset of inhibition potency and pharmacokinetic properties paired with multiple features of compounds was constructed. On this basis, the random forest method was utilized to choose greater-influenced feature embeddings; thus, 224 main operating variables were selected for further analysis; we then employed the efficient MobileNetV3 deep neural network as the backbone to establish the prediction models for the inhibition potency and pharmacokinetic properties of the compounds. After data preprocessing, the weights are obtained by training on the refined dataset. Finally, we define an optimization problem to discover compounds with the best properties. The problem is solved using the genetic algorithm with the acquired prediction model, and the solution value for the corresponding operating variables with the best clinical properties in theory is then obtained. Analysis demonstrates that our approach could be used to aid the screening process of antibreast cancer drug candidates.