A Graph-Based Clustering Analysis of the QM9 Dataset via SMILES Descriptors. - Gabriel A. Pinheiro; Juarez L. F. Da Silva; Marinalva D. Soares; Marcos G. Quiles (ICCSA (1), 2020)

Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition. - Luis Cesar de Azevedo; Gabriel A. Pinheiro; Marcos G. Quiles; Juarez L. F. Da Silva; Ronaldo C. Prati (J. Chem. Inf. Model., 2021)

The impact of low-cost molecular geometry optimization in property prediction via graph neural network. - Gabriel A. Pinheiro; Felipe V. Calderan; Juarez L. F. Da Silva; Marcos G. Quiles (ICMLA, 2022)

Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining. - Alex S. Moraes; Gabriel A. Pinheiro; Tuanan C. Lourenço; Mauro C. Lopes; Marcos G. Quiles; Luis Gustavo Dias; Juarez L. F. Da Silva (J. Chem. Inf. Model., 2022)

SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning. - Gabriel A. Pinheiro; Juarez L. F. Da Silva; Marcos G. Quiles (J. Chem. Inf. Model., 2022)

Enhancing Low-Cost Molecular Property Prediction with Contrastive Learning on SMILES Representations. - Marcos G. Quiles; Piero A. L. Ribeiro; Gabriel A. Pinheiro; Ronaldo C. Prati; Juarez L. F. Da Silva (ICCSA (Workshops 9), 2024)