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

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

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...

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Bibliographic Details
Main Author: Le, Thi Thu Huong
Format: Article
Language:English
Published: Mol2Net 2016
Subjects:
Atom-based quadratic index
Classification and regression model
Machine learning
Proteasome inhibition
QSAR
TOMOCOMD-CARDD software
Online Access:http://repository.vnu.edu.vn/handle/VNU_123/11510
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Institution: Vietnam National University, Hanoi
Language: English
Description
Summary: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

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