title: SVM approach for predicting LogP.
abstract: The logarithm of the partition coefficient between n-octanol and water (logP) is an important parameter for drug discovery. Based upon the comparison of several prediction logP models, i.e. Support Vector Machines (SVM), Partial Least Squares (PLS) and Multiple Linear Regression (MLR), the authors reported SVM model is the best one in this paper.
authors.affiliation: Department of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Quan
authors.initials: Q
authors.lastname: Liao
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
authors.forename: Shengang
authors.initials: S
authors.lastname: Yuan
mesh_terms: D001185:Artificial Intelligence; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D003936:Diagnosis, Computer-Assisted; D016018:Least-Squares Analysis; D016014:Linear Models; D008956:Models, Chemical; D008962:Models, Theoretical; D000442:Octanols / Q000737:chemistry; D004364:Pharmaceutical Preparations / Q000145:classification; D012995:Solubility; D014867:Water / Q000737:chemistry
chemical_list: D000442:Octanols; D004364:Pharmaceutical Preparations; D014867:Water