改进的粒子群优化算法用于芳香化合物毒性的定量构效关系分析

QSAR Study of Carcinogenicity of Aromatic Amines by Modified Particle Swarm Optimization Algorithm

粒子群优化(PSO)算法是一种新兴的优化技术,该算法简单易实现,可调参数少,已广泛应用于许多研究领域,但PSO在化学中的应用还较少。本文将改进的粒子群优化算法用于多元线性回归的变量选择,并将其用于芳香胺的致癌活性的构效关系研究,结果表明:改进的粒子群优化算法能搜索到最优的变量组合,具有较快的收敛速度。

Particle swarm optimization (PSO) algorithm is a new optimization technique.The advantages of PSO are that PSO is easy to implement and there are few parameters to adjust.PSO has been successfully applied in many areas.To our knowledge,there were few reports concerning the application of PSO in quantitative structure-activity relationships(QSARs). In this paper,the modified discrete PSO algorithm is used to select variables in multiple linear regression modeling and to predict carcinogenicity of aromatic amines.Experimental results show that the modified PSO is a useful tool for variable selection which converges quickly towards the optimal position.