Open AccessClassification of drug molecules for oxidative stress signalling pathway
Author(s) -
Verma Nikhil,
Singh Harpreet,
Khanna Divya,
Rana Prashant Singh,
Bhadada Sanjay Kumar
Publication year - 2019
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2018.5078
Subject(s) - oxidative stress , signalling , drug discovery , antioxidant , diabetes mellitus , potency , computational biology , pharmacology , computer science , machine learning , bioinformatics , medicine , chemistry , biology , biochemistry , microbiology and biotechnology , in vitro , endocrinology
In humans, oxidative stress is involved in the development of diabetes, cancer, hypertension, Alzheimers’ disease, and heart failure. One of the mechanisms in the cellular defence against oxidative stress is the activation of the Nrf2‐antioxidant response element (ARE) signalling pathway. Computation of activity, efficacy, and potency score of ARE signalling pathway and to propose a multi‐level prediction scheme for the same is the main aim of the study as it contributes in a big amount to the improvement of oxidative stress in humans. Applying the process of knowledge discovery from data, required knowledge is gathered and then machine learning techniques are applied to propose a multi‐level scheme. The validation of the proposed scheme is done using the K‐fold cross‐validation method and an accuracy of 90% is achieved for prediction of activity score for ARE molecules which determine their power to refine oxidative stress.