基于神经网络抗乳腺癌候选药物优化建模

Optimization Modeling of Anti Breast Cancer Drug Candidates Based on Neural Network

摘要: ERα在乳腺发育过程中具有十分重要的作用，被认为是治疗乳腺癌的重要靶标。在化合物候选过程中，其不仅需要具备良好的生物活性，还需要具备良好的药代动力学性质和安全性。本文采用数据挖掘技术，根据提供的ERα拮抗剂信息，构建化合物生物活性的定量预测模型和ADMET性质的分类预测模型，从而为同时优化ERα拮抗剂的生物活性和ADMET性质提供预测服务，具有一定的现实意义。

Abstract: ERα plays a very important role in breast development and is considered as an important target for the treatment of breast cancer. In the candidate process of compounds, they need not only good biological activity, but also good pharmacokinetic properties and safety. In this paper, data mining technology is adopted according to the provided ERα Antagonist information, to build a quantitative prediction model of compound biological activity and a classification prediction model of ADMET properties, so as to simultaneously optimize ERα. The biological activity and ADMET properties of antagonists provide prediction services, which has certain practical significance.