Paper Info
Title
Drug Side-effect Profiles Prediction: From Empirical Risk Minimization to Structural Risk Minimization.
Abstract
The identification of drug side-effects is considered to be an important step in drug design, which could not only shorten the time but also reduce the cost of drug development. In this paper, we investigate the relationship between the potential side-effects of drug candidates and their chemical structures. The preliminary Regularized Regression (RR) model for drug side-effects prediction has promising features in the efficiency of model training and the existence of a closed form solution. It performs better than other state-of-the-art methods, in terms of minimum accuracy and average accuracy. In order to dig inside how drug structure will associate with side effect, we further propose weighted GTS (Generalized T-Student Kernel: WGTS) SVM model from a structural risk minimization perspective. The SVM model proposed in this paper provides a better understanding of drug side-effects in the process of drug development. The usefulness of the WGTS model lies in the superior performance in a cross validation setting on 888 approved drugs with 1385 side-effects profiling from SIDER database. This work is expected to shed light on intriguing studies that predict potential un-identifying side-effects and suggest how we can avoid drug side-effects by the removal of some distinguished chemical structures.
Year
DOI
Venue
2020
10.1109/TCBB.2018.2850884
IEEE/ACM transactions on computational biology and bioinformatics
Keywords
DocType
Volume
Drugs,Support vector machines,Predictive models,Chemicals,Kernel,Databases,Proteins
Journal
17
Issue
ISSN
Citations 
2
1545-5963
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Hao Jiang100.68
Yushan Qiu2206.28
Wenpin Hou321.42
Xiaoqing Cheng400.34
Manyi Yim500.34
Wai-Ki Ching668378.66