Renata De Paris
Renata De Paris
Researcher, School of Technology, PUCRS
Verified email at acad.pucrs.br
Title
Cited by
Cited by
Year
wFReDoW: a cloud-based web environment to handle molecular docking simulations of a fully flexible receptor model
R De Paris, FA Frantz, O Norberto de Souza, DDA Ruiz
BioMed research international 2013, 2013
322013
Clustering molecular dynamics trajectories for optimizing docking experiments
R De Paris, CV Quevedo, DD Ruiz, O Norberto de Souza, RC Barros
Computational intelligence and neuroscience 2015, 2015
292015
An effective approach for clustering InhA molecular dynamics trajectory using substrate-binding cavity features
R De Paris, CV Quevedo, DDA Ruiz, O Norberto de Souza
PloS one 10 (7), e0133172, 2015
212015
A strategic solution to optimize molecular docking simulations using fully-flexible receptor models
CV Quevedo, R De Paris, DD Ruiz, ON de Souza
Expert Systems with Applications 41 (16), 7608-7620, 2014
172014
A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model
R De Paris, CV Quevedo, DD Ruiz, F Gargano, ON de Souza
BMC bioinformatics 19 (1), 1-16, 2018
72018
FReMI: a middleware to handle molecular docking simulations of fully-flexible receptor models in HPC environments
R De Paris
Pontifícia Universidade Católica do Rio Grande do Sul, 2012
72012
SMARTIX: A database indexing agent based on reinforcement learning
GP Licks, JC Couto, P de Fátima Miehe, R De Paris, DD Ruiz, ...
Applied Intelligence, 1-14, 2020
42020
A cloud-based workflow approach for optimizing molecular docking simulations of fully-flexible receptor models and multiple ligands
R De Paris, DAD Ruiz, ON De Souza
2015 IEEE 7th International Conference on Cloud Computing Technology and …, 2015
32015
A conceptual many tasks computing architecture to execute molecular docking simulations of a fully-flexible receptor model
R De Paris, FA Frantz, ON de Souza, DD Ruiz
Brazilian Symposium on Bioinformatics, 75-78, 2011
22011
An effective method to optimize docking-based virtual screening of fully-flexilbe receptor models
R De Paris, CV Quevedo, DA Ruiz, O Norberto de Souza
32nd Brazilian Symposium on Databases (SBBD). Minas Gerais: Brazilian …, 2017
12017
Clustering molecular dynamics trajectories with a univariate estimation of distribution algorithm
RC Barros, CV Quevedo, R De Paris, MP Basgalupp
2015 IEEE Congress on Evolutionary Computation (CEC), 2058-2065, 2015
12015
An effective method to optimize docking-based virtual screening in a clustered fully-flexible receptor model deployed on cloud platforms
R De Paris
Pontifícia Universidade Católica do Rio Grande do Sul, 2017
2017
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Articles 1–12