Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction
http://repository.vnu.edu.vn/handle/VNU_123/11510 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 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-CA...