Back to Search Start Over

QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models

Authors :
Amit Kumar Halder
Humberto González Díaz
M. Natália D. S. Cordeiro
Pravin Ambure
Source :
Journal of Chemical Information and Modeling. 59:2538-2544
Publication Year :
2019
Publisher :
American Chemical Society (ACS), 2019.

Abstract

Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.

Details

ISSN :
1549960X and 15499596
Volume :
59
Database :
OpenAIRE
Journal :
Journal of Chemical Information and Modeling
Accession number :
edsair.doi.dedup.....b3067e9951996089ad867515ffa0aeff
Full Text :
https://doi.org/10.1021/acs.jcim.9b00295