人工神经网络
抗菌剂
生物系统
人工智能
数量结构-活动关系
回归
大肠杆菌
机器学习
集合(抽象数据类型)
线性回归
生化工程
数据集
咪唑
计算机科学
数学
化学
生物
工程类
生物化学
统计
有机化学
基因
程序设计语言
作者
Anna Badura,Jerzy Krysiński,Alicja Nowaczyk,Adam Buciński
摘要
This article presents models of artificial neural networks (ANN) employed to predict the biological activity of chemical compounds based of their structure. Regression and classification models were designed to determine antimicrobial properties of quaternary ammonium salts against Escherichia coli strain. The minimum inhibitory concentration microbial growth E. coli was experimentally determined by the serial dilution method for a series of 140 imidazole derivatives. Then, three‐dimensional models for imidazole chlorides were constructed with computational chemistry methods which allowed to calculate molecular descriptors. The transformation of chemical information into a useful number is a main result of this operation. The designed regression and classification ANN models were characterized by a high predictive ability (classification accuracy was 95%, regression model: learning set R = 0.87, testing set R = 0.91, validation set R = 0.89). Artificial neural networks can be successfully used to find potential antimicrobial preparations. The neural networks are a very elaborate modelling technique, which allows not only to optimize and minimize labour costs but also to increase food safety.
科研通智能强力驱动
Strongly Powered by AbleSci AI