Artificial Intelligence in Biological Activity Prediction. - João Correia 002; Tiago Resende; Delora Baptista; Miguel Rocha 001 (PACBB, 2019)

Combining Multi-objective Evolutionary Algorithms with Deep Generative Models Towards Focused Molecular Design. - Tiago Sousa; João Correia 002; Vitor Pereira 001; Miguel Rocha 001 (EvoApplications, 2021)

A Comparison of Different Compound Representations for Drug Sensitivity Prediction. - Delora Baptista; João Correia 002; Bruno Pereira; Miguel Rocha 001 (PACBB, 2021)

Generative Deep Learning for Targeted Compound Design. - Tiago Sousa; João Correia 002; Vitor Pereira 001; Miguel Rocha 001 (J. Chem. Inf. Model., 2021)

Development of Deep Learning approaches to predict relationships between chemical structures and sweetness. - João Capela; João Correia 002; Vitor Pereira 001; Miguel Rocha 001 (IJCNN, 2022)

Predicting the number of biochemical transformations needed to synthesize a compound. - João Correia 002; Rafael Carreira; Vitor Pereira 001; Miguel Rocha 001 (IJCNN, 2022)

Evaluating molecular representations in machine learning models for drug response prediction and interpretability. - Delora Baptista; João Correia 002; Bruno Pereira; Miguel Rocha 001 (J. Integr. Bioinform., 2022)

Combining Evolutionary Algorithms with Reaction Rules Towards Focused Molecular Design. - João Correia 002; Vitor Pereira 001; Miguel Rocha 001 (GECCO, 2023)

DeepRetro: a computational framework for retrosynthesis and pathway design towards optimizing compound bioproduction - João Correia 002 (2024)

Deepmol: an automated machine and deep learning framework for computational chemistry. - João Correia 002; João Capela; Miguel Rocha 001 (J. Cheminformatics, 2024)