卷积神经网络在药学领域的创新应用  

Innovative Application of Convolutional Neural Networks in the Field of Pharmacy

作　　者：常富超 CHANG Fuchao(Zhengzhou Institute of Industrial Application Technology,Xinzheng Henan 451100)

机构地区：[1]郑州工业应用技术学院,河南新郑451100

出　　处：《软件》2025年第1期82-84,共3页Software

摘　　要：本文旨在探讨卷积神经网络(Convolution Neural Network,CNN)的强大特征提取能力,实现对药物分子结构的分析、药物活性预测以及药物图像识别等任务。方法包括收集药物分子结构数据、药物活性数据以及药物图像数据,构建适合药学领域的CNN模型,并进行训练和优化。实验结果表明,CNN在药物分子结构分析、药物活性预测以及药物图像识别等方面均取得了较好的效果。其在药学领域具有巨大的应用潜力,可以为药物研发、药物质量控制等提供新的技术手段。This article aims to discuss the powerful feature extraction capabilities of the Convolution Neural Network(CNN)to achieve tasks such as analysis of drug molecular structure,drug activity prediction,and drug image recognition.Methods include collecting pharmaceutical molecular structure data,drug activity data,and pharmaceutical image data,build a CNN model suitable for pharmacy,and conduct training and optimization.The experimental results show that CNN has achieved good results in terms of drug molecular structure analysis,drug activity prediction,and drug image recognition.It has huge application potential in the field of pharmacy,and can provide new technical means for drug research and development and drug quality control.

关 键 词：卷积神经网络 药物活性 药物分子 结构数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]