J
Jadson Castro Gertrudes
Researcher at University of São Paulo
Publications - 14
Citations - 339
Jadson Castro Gertrudes is an academic researcher from University of São Paulo. The author has contributed to research in topics: Quantitative structure–activity relationship & Cluster analysis. The author has an hindex of 6, co-authored 12 publications receiving 241 citations. Previous affiliations of Jadson Castro Gertrudes include Universidade Federal de Ouro Preto.
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Journal ArticleDOI
Machine learning techniques and drug design.
Jadson Castro Gertrudes,Vinícius Gonçalves Maltarollo,Ricardo Silva,Patrícia R. Oliveira,Káthia Maria Honório,A. B. F. da Silva +5 more
TL;DR: A critical point of view on the main MLT shows their potential ability as a valuable tool in drug design and shows that MLT have significant advantages.
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Applying machine learning techniques for ADME-Tox prediction: a review.
Vinícius Gonçalves Maltarollo,Jadson Castro Gertrudes,Patrícia R. Oliveira,Káthia Maria Honório +3 more
TL;DR: An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure–activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.
Journal ArticleDOI
Molecular docking studies and 2D analyses of DPP-4 inhibitors as candidates in the treatment of diabetes.
Simone Queiroz Pantaleão,Vinícius Gonçalves Maltarollo,Sheila C. Araujo,Jadson Castro Gertrudes,Káthia Maria Honório,Káthia Maria Honório +5 more
TL;DR: The final model constructed in this study could be useful in the design of novel DPP-4 ligands with improved activity, as the predictive power of this model for untested compounds is indicated.
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Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates.
Mateus Sá Magalhães Serafim,Jadson Castro Gertrudes,Débora Maria Abrantes Costa,Patrícia R. Oliveira,Vinícius Gonçalves Maltarollo,Káthia Maria Honório,Káthia Maria Honório +6 more
TL;DR: In this paper, the authors describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.
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A unified view of density-based methods for semi-supervised clustering and classification.
TL;DR: This paper shows that there are close relations between density-based clustering algorithms and the graph-based approach for transductive classification and builds upon this view to bridge the areas of semi-supervised clustering and classification under a common umbrella ofdensity-based techniques.