New Workflow for QSAR Model Development from Small Data Sets: Small Dataset Curator and Small Dataset Modeler. Integration of Data Curation, Exhaustive Double Cross-Validation, and a Set of Optimal Model Selection Techniques

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

Authors

Ambure, P (Author) Other; Gajewicz Skretna, A (Author) Other; Natalia N D S Cordeiro (Author) FCUP; Roy, K (Author) Other

Journal

Title: Journal of Chemical Information and Modeling

Vol. 59

Pages: 4070-4076

ISSN: 1549-9596

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 30 Citations

Scopus - 36 Citations

Other information

Authenticus ID: P-00R-5F1

DOI: 10.1021/acs.jcim.9b00476

Abstract (EN): Quantitative structure activity relationship (QSAR) modeling is a well-known in silico technique with extensive applications in several major fields such as drug design, predictive toxicology, materials science, food science, etc. Handling small-sized datasets due to the lack of experimental data for specialized end points is a crucial task for the QSAR researcher. In the present study, we propose an integrated workflow/scheme capable of dealing with small dataset modeling that integrates dataset curation, "exhaustive" double cross-validation and a set of optimal model selection techniques including consensus predictions. We have developed two software tools, namely, Small Dataset Curator, version 1.0.0, and Small Dataset Modeler, version 1.0.0, to effortlessly execute the proposed workflow. These tools are freely available for download from https://dtclab.webs.com/software-tools. We have performed case studies employing seven diverse datasets to demonstrate the performance of the proposed scheme (including data curation) for small dataset QSAR modeling. The case studies also confirm the usability and stability of the developed software tools.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7