A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.
A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.
a Department of Mathematical Sciences , Universiti Teknologi Malaysia , Skudai , Johor , Malaysia.
Z Y
ZY
Algamal
a Department of Mathematical Sciences , Universiti Teknologi Malaysia , Skudai , Johor , Malaysia.
M H
MH
Lee
D000998:Antiviral Agents / Q000737:chemistry* / Q000145:classification / Q000494:pharmacology*; D016174:Hepacivirus / Q000187:drug effects*; D021281:Quantitative Structure-Activity Relationship; D013890:Thiourea / Q000031:analogs & derivatives* / Q000145:classification / Q000494:pharmacology*
D000998:Antiviral Agents; D013890:Thiourea
QSAR; classification; lasso; penalized logistic regression; penalized method