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QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models
- 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.
- Subjects :
- Quantitative structure–activity relationship
Computer science
General Chemical Engineering
Quantitative Structure-Activity Relationship
Library and Information Sciences
Machine learning
computer.software_genre
01 natural sciences
Computational Technique
Software
Drug Discovery
0103 physical sciences
Still face
Humans
Human multitasking
010304 chemical physics
business.industry
Discriminant Analysis
General Chemistry
Open source software
Linear discriminant analysis
0104 chemical sciences
Computer Science Applications
Random forest
010404 medicinal & biomolecular chemistry
Drug Design
Artificial intelligence
business
computer
Subjects
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