Author

Renata De Paris

Observed names: De Paris, Renata, Paris, Renata De, Renata de Paris

Institution: Instituto de Pesquisas Eldorado

Observed institutions: Instituto de Pesquisas Eldorado, Pontifícia Universidade Católica do Rio Grande do Sul

ORCID: 0000-0002-6662-2182

Observed ORCID: 0000-0002-6662-2182

OpenAlex ID: A5047789522

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

2015 · Renata De Paris, Christian V. Quevedo, et al. · Computational Intelligence and Neuroscience · 67

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SmartIX: A database indexing agent based on reinforcement learning

2020 · Gabriel Paludo Licks, Julia Colleoni Couto, et al. · Applied Intelligence · 46

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An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features

2015 · Renata De Paris, Christian V. Quevedo, et al. · PLoS ONE · 35

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wFReDoW: A Cloud-Based Web Environment to Handle Molecular Docking Simulations of a Fully Flexible Receptor Model

2013 · Renata De Paris, Fábio A. Frantz, et al. · BioMed Research International · 29

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A strategic solution to optimize molecular docking simulations using Fully-Flexible Receptor models

2014 · Christian Vahl Quevedo, Renata De Paris, et al. · Expert Systems with Applications · 18

A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

2018 · Renata De Paris, Christian Vahl Quevedo, et al. · BMC Bioinformatics · 13

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eXplainable Artificial Intelligence on Medical Images: A Survey

2023 · da Silva, Matteus Vargas Simão, Arrais, Rodrigo Reis, et al. · arXiv (Cornell University) · 10

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A Cloud-Based Workflow Approach for Optimizing Molecular Docking Simulations of Fully-Flexible Receptor Models and Multiple Ligands

2015 · Renata De Paris, Duncan A. D. Ruiz, et al. · IEEE International Conference on Cloud Computing Technology and Science · 4

A Conceptual Many Tasks Computing Architecture to Execute Molecular Docking Simulations of a Fully-Flexible Receptor Model

2011 · Renata De Paris, Fábio A. Frantz, et al. · Lecture notes in computer science · 3

FReMI: a middleware to handle molecular docking simulations of fully-flexible receptor models in HPC environments

2012 · Renata De Paris · Americanae (AECID Library) · 1

Memory-Aware Multi-agent Architecture with LLM-Based Orchestration for Conversational Recommendation

2026 · Emilly Pitman de Castro, Vanessa Silva, et al. · Lecture notes in computer science

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Accelerating ligand screening for tuberculosis: predicting binding energies with a 3D deep neural network

2026 · Duncan Dubugras A. Ruiz, Renata De Paris, et al. · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics

HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation

2026 · Moraes, Abriel K., Dias, Gabriel S. M., et al. · arXiv (Cornell University)

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From Docking to Deep Learning: A 3D Neural Network for Accelerating Ligand Screening

2026 · Duncan Dubugras A. Ruiz, Renata De Paris, et al. · Lecture notes in networks and systems

Comparing Automatic Speech Recognition Quality for Brazilian Portuguese in Multimodal Large Models

2026 · Lucas Lanaro, Gabriel Moraes, et al. · International Conference on Agents and Artificial Intelligence (ICAART)

Comparing RAG, DPO and Agentic Approaches in Systems Performance on Q&A about Brazilian Labor Legislation

2026 · Gabriel Moraes, Pedro Luiz, et al. · International Conference on Agents and Artificial Intelligence (ICAART)

HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation

2025 · Keglevich Moraes, Gabriel, Santos Martin Dias, Gabriel, et al. · Anais do ... Simpósio Brasileiro de Automação Inteligente/Simpósio Brasileiro de Automação Inteligente

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HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation

2025 · Gabriel Keglevich Moraes, Gabriel Santos Martin Dias, et al. · Anais do ... Simpósio Brasileiro de Sistemas Elétricos

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Explainable Artificial Intelligence Using Forward-Forward Networks: A Study Involving Quantitative Analysis

2024 · Vitor L. Fabris, Juliane R. de Oliveira, et al.

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Efficient Brazilian Sign Language Recognition: A Study on Mobile Devices

2023 · Vitor Lopes Fabris, Felype de Castro Bastos, et al. · Lecture notes in computer science

Additional file 1 of A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

2018 · Paris, Renata De, Quevedo, Christian Vahl, et al. · Figshare

An effective method to optimize docking-based virtual screening in a clustered fully-flexible receptor model deployed on cloud platforms