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...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Article |
| 語言: | English |
| 出版: |
Mol2Net
2016
|
| 主題: | |
| 在線閱讀: | http://repository.vnu.edu.vn/handle/VNU_123/11510 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | 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 |
|---|