Open AccessA prediction model for oral bioavailability of drugs using physicochemical properties by support vector machine
Author(s) -
Rajnish Kumar,
Anju Sharma,
Pritish Kumar Varadwaj
Publication year - 2011
Publication title -
journal of natural science, biology and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.236
H-Index - 30
eISSN - 2229-7707
pISSN - 0976-9668
DOI - 10.4103/0976-9668.92325
Subject(s) - support vector machine , bioavailability , test set , nonparametric statistics , artificial intelligence , computer science , classifier (uml) , machine learning , receiver operating characteristic , cheminformatics , quantitative structure–activity relationship , data mining , mathematics , statistics , chemistry , pharmacology , medicine , computational chemistry
A computational model for predicting oral bioavailability is very important both in the early stage of drug discovery to select the promising compounds for further optimizations and in later stage to identify candidates for clinical trials. In present study, we propose a support vector machine (SVM)-based kernel learning approach carried out at a set of 511 chemically diverse compounds with known oral bioavailability values.