DataWarrior: an evaluation of the open-source drug discovery tool

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.

[1]  Nikolai S. Zefirov,et al.  Progress in visual representations of chemical space , 2015, Expert opinion on drug discovery.

[2]  Qamar Abbas,et al.  Pharmacoinformatics exploration of polyphenol oxidases leading to novel inhibitors by virtual screening and molecular dynamic simulation study , 2017, Comput. Biol. Chem..

[3]  A. Trabocchi,et al.  Diversity-Oriented Synthesis and Chemoinformatic Analysis of the Molecular Diversity of sp3-Rich Morpholine Peptidomimetics , 2018, Front. Chem..

[4]  L Xue,et al.  Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. , 2000, Combinatorial chemistry & high throughput screening.

[5]  José L. Medina-Franco,et al.  Activity Landscape Plotter: A Web-Based Application for the Analysis of Structure-Activity Relationships , 2017, J. Chem. Inf. Model..

[6]  Thomas Sander,et al.  DataWarrior: An Open-Source Program For Chemistry Aware Data Visualization And Analysis , 2015, J. Chem. Inf. Model..

[7]  Fereshteh Shiri,et al.  Anti-cancer study and whey protein complexation of new lanthanum(III) complex with the aim of achieving bioactive anticancer metal-based drugs , 2018, Journal of biomolecular structure & dynamics.

[8]  Abraham Yosipof,et al.  Materials Informatics: Statistical Modeling in Material Science , 2016, Molecular informatics.

[9]  J. Jesús Naveja,et al.  Activity landscape sweeping: insights into the mechanism of inhibition and optimization of DNMT1 inhibitors , 2015 .

[10]  José L. Medina-Franco,et al.  A chemical space odyssey of inhibitors of histone deacetylases and bromodomains , 2016 .

[11]  Thomas Sander,et al.  Flexophore, a New Versatile 3D Pharmacophore Descriptor That Considers Molecular Flexibility , 2008, J. Chem. Inf. Model..

[12]  José L Medina-Franco,et al.  Platform for Unified Molecular Analysis: PUMA , 2017, J. Chem. Inf. Model..

[13]  Olivier Sperandio,et al.  One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade. , 2013, Drug discovery today.

[14]  Aaron Sim,et al.  Big Data in Drug Discovery. , 2018, Progress in medicinal chemistry.

[15]  Diego Molina,et al.  In silico study of Moxifloxacin derivatives with possible antibacterial activity against a resistant form of DNA gyrase from Porphyromonas gingivalis. , 2018, Archives of oral biology.

[16]  Daniel Probst,et al.  WebMolCS: A Web-Based Interface for Visualizing Molecules in Three-Dimensional Chemical Spaces , 2017, J. Chem. Inf. Model..

[17]  Woody Sherman,et al.  Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments , 2010, J. Chem. Inf. Model..

[18]  Alexander Tropsha,et al.  Materials Informatics , 2019, J. Chem. Inf. Model..

[19]  P. Graczyk Gini coefficient: a new way to express selectivity of kinase inhibitors against a family of kinases. , 2007, Journal of medicinal chemistry.

[20]  Alexander Tropsha,et al.  Chembench: A Publicly Accessible, Integrated Cheminformatics Portal , 2017, J. Chem. Inf. Model..

[21]  Alexander Tropsha,et al.  Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides? , 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[22]  Peter Murray-Rust,et al.  Chemical Name to Structure: OPSIN, an Open Source Solution , 2011, J. Chem. Inf. Model..

[23]  A. Christoffels,et al.  Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs , 2016, Malaria Journal.

[24]  José L Medina-Franco,et al.  Chemoinformatics: a perspective from an academic setting in Latin America , 2017, Molecular Diversity.

[25]  Sagarika Sahoo,et al.  A Short Review of the Generation of Molecular Descriptors and Their Applications in Quantitative Structure Property/Activity Relationships. , 2016, Current computer-aided drug design.

[26]  José L Medina-Franco,et al.  Activity Landscape and Molecular Modeling to Explore the SAR of Dual Epigenetic Inhibitors: A Focus on G9a and DNMT1 , 2018, Molecules.

[27]  Emilio Benfenati,et al.  A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications , 2018, Journal of Cheminformatics.

[28]  Woody Sherman,et al.  Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods , 2010, J. Cheminformatics.

[29]  J. Jesús Naveja,et al.  Open chemoinformatic resources to explore the structure, properties and chemical space of molecules , 2017 .