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

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Ambure, P (Author) Other; Halder, AK (Author) Other; Gonzalez Diaz, HG (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: Journal of Chemical Information and Modeling

Vol. 59

Pages: 2538-2544

ISSN: 1549-9596

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 41 Citations

Scopus - 43 Citations

Other information

Authenticus ID: P-00Q-NYZ

DOI: 10.1021/acs.jcim.9b00295

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7