EDGAR LÓPEZ LÓPEZ

Dr. EDGAR LÓPEZ LÓPEZ

Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Stockholm, Sweden
orcid

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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26 February 2019 in Expert Opinion on Drug Discovery
Verified
Journal Article
Closed access
EDGAR LÓPEZ LÓPEZEDGAR LÓPEZ LÓPEZ
EDGAR LÓPEZ LÓPEZ
JNJosé J. Naveja
José J. Naveja
JMJose Medina-Franco
Jose Medina-Franco
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 January 2019
Verified
Book Chapter
Closed access
FPFernando Daniel Prieto-Martínez
Fernando Daniel Prieto-Martínez
EDGAR LÓPEZ LÓPEZEDGAR LÓPEZ LÓPEZ
EDGAR LÓPEZ LÓPEZ
KJK. Eurídice Juárez-Mercado
K. Eurídice Juárez-Mercado
JMJosé L. Medina-Franco
José L. Medina-Franco

31 December 2020 in Journal of Chemical Information and Modeling
Verified
Journal Article
Closed access
EDGAR LÓPEZ LÓPEZEDGAR LÓPEZ LÓPEZ
EDGAR LÓPEZ LÓPEZ
Jürgen BajorathJürgen Bajorath
Jürgen Bajorath
JMJose Medina-Franco
Jose Medina-Franco

18 June 2021 in Journal of Computer-Aided Molecular Design
Verified
Journal Article
Closed access
PDF file
JMJose Medina-Franco
Jose Medina-Franco
NSNorberto Sánchez-Cruz
Norberto Sánchez-Cruz
EDGAR LÓPEZ LÓPEZEDGAR LÓPEZ LÓPEZ
EDGAR LÓPEZ LÓPEZ
BEBarbara Itzel Diaz Eufracio
Barbara Itzel Diaz Eufracio

11 May 2022 in Drug Discovery Today
Verified
Journal Article
Closed access
EDGAR LÓPEZ LÓPEZEDGAR LÓPEZ LÓPEZ
EDGAR LÓPEZ LÓPEZ
EGEli Fernández-De Gortari
Eli Fernández-De Gortari
JMJose Medina-Franco
Jose Medina-Franco

7 June 2023 in Molecular Informatics
Verified
Journal Article
Open access
TDTimothy B. Dunn
Timothy B. Dunn
EDGAR LÓPEZ LÓPEZEDGAR LÓPEZ LÓPEZ
EDGAR LÓPEZ LÓPEZ
TKTaewon David Kim
Taewon David Kim
JMJose Medina-Franco
Jose Medina-Franco
RQRamon Alain Miranda Quintana
Ramon Alain Miranda Quintana
Understanding structure‐activity landscapes is essential in drug discovery. Similarly, it has been shown that the presence of activity cliffs in compound data sets can have a substantial impact not only on the design progress but also can influence the predictive ability of machine learning models. With the continued expansion of the chemical space and the currently available large and ultra‐large libraries, it is imperative to implement efficient tools to analyze the activity landscape of compound data sets rapidly. The goal of this study is to show the applicability of the n‐ary indices to quantify the structure‐activity landscapes of large compound data sets using different types of structural representation rapidly and efficiently. We also discuss how a recently introduced medoid algorithm provides the foundation to finding optimum correlations between similarity measures and structure‐activity rankings. The applicability of the n‐ary indices and the medoid algorithm is shown by analyzing the activity landscape of 10 compound data sets with pharmaceutical relevance using three fingerprints of different designs, 16 extended similarity indices, and 11 coincidence thresholds.