基于GA-BP神经网络模型的抗乳腺癌候选药物活性预测  

Prediction of Active Value of Anti-breast Cancer Drug Candidates Based on GA-BP Neural Network Model

作　　者：尚雅欣 雷小洁 方子牛 张宏伟[1] Shang Yaxin;Lei Xiaojie;Fang Ziniu;Zhang Hongwei(School of Mathematics and Statistics,Central South University,Changsha 410083,China)

机构地区：[1]中南大学数学与统计学院,长沙410083

出　　处：《数学理论与应用》2024年第2期103-125,共23页Mathematical Theory and Applications

摘　　要：抗乳腺癌候选药物筛选对治疗乳腺癌意义重大.乳腺癌的抗激素治疗常用于ERα表达的乳腺癌患者,抗ERα活性值越高代表该药物对治疗乳腺癌越有效.因此,精准预测化合物的抗ERα活性值至关重要.本文首先对化合物的729个分子描述符特征使用梯度提升模型XGBoost和距离相关系数矩阵进行筛选,然后基于筛选的20个分子描述符及其活性值数据,引入遗传算法,建立GA-BP神经网络模型.该模型的均方误差MSE=0.105,拟合优度R2=0.946,是一个基于数据挖掘技术的筛选潜在药物的高精度模型.Screening for anti-breast cancer drug candidates is of great significance in the treatment of breast cancer.Antihormonal therapy for breast cancer is often used in breast cancer patients with ERαexpression,and the higher the anti-ERαactivity value,the more effective the drug is for treatment.In this paper,729 molecular descriptors of the compound are filtered firstly using the gradient boost model XGBoost and the distance correlation coefficient matrix,then based on the filtered 20 molecular descriptors with their activity values and the genetic algorithm,a GA-BP neural network model is established,which has a mean squared error MSE=0.105 and a coefficient of determination R~2=0.946,and therefore is a high-precision model for screening potential drugs based on data mining techniques.

关 键 词：抗乳腺癌药物筛选 距离相关系数 XGBoost算法 GA-BP神经网络 均方误差 

分 类 号：R965.1[医药卫生—药理学] TP18[医药卫生—药学]