关键词: 支持向量机（SVM）, 人血清白蛋白（HSA）, 遗传算法（GA）, 粒子群优化算法（PSO）, 双线性网格搜索法（BGSM）

Abstract: In order to reduce the rate of the failure of the new drugs’ clinical experiment and enhance drugs’ ADME/T properties，it’s necessary to consider the combinative activity of drugs and HSA（Human Serum Albumin） and increase the prediction accuracy of drugs and HSA，so that proper components can be chosen to make new drugs.Genetic algorithm was used to choose molecular descriptors and Support Vector Machine classification method was employed to predict the combinative activity between drugs and HSA.On the basis of research in SVM，the method of searching optimized SVM parameter by PSO（Particle Swarm Optimization） algorithm was presented and the ends were compared with Bilinear Grid Search Method.It is shown that this model has a good prediction ability and the accuracy is 86%.

Key words: Support Vector Machine（SVM）, Human Serum Albumin（HSA）, Genetic Algorithm（GA）, Particle Swarm Optimization（PSO）, Bilinear Grid Search Method（BGSM）