Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction

Title:	Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction
Authors:	Le, Thi Thu Huong
Keywords:	Atom-based quadratic index, Classification and regression model, Machine learning, Proteasome inhibition, QSAR, TOMOCOMD-CARDD software
Issue Date:	2015
Publisher:	Mol2Net
Abstract:	The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures
URI:	http://repository.vnu.edu.vn/handle/VNU_123/11510
ISSN:	1422-0067
Appears in Collections:	SMP - Papers / Tham luận HN-HT