ABSTRACT
Introduction: DataWarrior is open and interactive software for data analysis and visualization that integrates well-established and novel chemoinformatics algorithms in a single environment. Since its public release in 2014, DataWarrior has been used by research groups in universities, government, and industry.
Areas covered: Herein, the authors discuss, in a critical manner, the tools and distinct technical features of DataWarrior and analyze the areas of opportunity. Authors also present the most common applications as well as emerging uses in research areas beyond drug discovery with an emphasis on multidisciplinary projects.
Expert opinion: In the era of big data and data-driven science, DataWarrior stands out as a technology that combines prediction of physicochemical properties of pharmaceutical interest, cheminformatics calculations, multivariate data analysis, and interactive visualization with dynamic plots. The well-established chemoinformatics tools implemented in DataWarrior, as well as the innovative algorithms, make the technology useful and attractive as revealed by the increasing number of documented applications.
Article highlights
DataWarrior is a free software running on different operating systems that support data-driven projects.
The software aids to visualize and analyze data to find associations between chemical structures and alphanumeric data.
The technology was developed to serve drug discovery projects which are the main applications. However, the software is started to be used in other areas of chemistry and biology.
DataWarrior is increasingly been used in academia, industry, and government.
The software is an important complement to available technologies. DataWarrior is easy to use (with a comprehensive User Manual and a friendly learning curve).
DataWarrior is used for basic and applied research, and for teaching.
In the era of big data and data-driven science, DataWarrior stands out as a technology that combines prediction of physicochemical properties of pharmaceutical interest, cheminformatics calculations, multivariate data analysis, and interactive visualization with dynamic plots.
Acknowledgments
The authors thank the financial support of Consejo Nacional de Ciencia y Tecnología, Mexico (CONACyT) (grant number 282785) and the Programa de Nuevas Alternativas de Tratamiento para Enfermedades Infecciosas (NUATEI-IIB-UNAM).
Declaration of interest
The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.