I can also be found on Google Scholar and Research Gate.
Publications in Peer-Reviewed Journals and Conferences
Last updated: 6th of June 2025
2024
Correia, J., Capela, J., & Rocha, M. (2024). Deepmol: an automated machine and deep learning framework for computational chemistry. Journal of Cheminformatics, 16(1), 136. https://doi.org/10.1186/s13321-024-00937-7
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 4 | 5 | 10 |
2023
Correia, J., Pereira, V., & Rocha, M. (2023). Combining evolutionary algorithms with reaction rules towards focused molecular design. Proceedings of the Genetic and Evolutionary Computation Conference, 900–909. https://doi.org/10.1145/3583131.3590413
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 0 | 0 | 2 |
2022
Baptista, D., Correia, J., Pereira, B., & Rocha, M. (2022b). Evaluating molecular representations in machine learning models for drug response prediction and interpretability. Journal of Integrative Bioinformatics, 19(3). https://doi.org/10.1515/jib-2022-0006
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 22 | 18 | 44 |
Correia, J., Carreira, R., Pereira, V., & Rocha, M. (2022). Predicting the number of biochemical transformations needed to synthesize a compound. 2022 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN55064.2022.9892124
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 1 | 1 | 2 |
Capela, J., Correia, J., Pereira, V., & Rocha, M. (2022). Development of Deep Learning approaches to predict relationships between chemical structures and sweetness. 2022 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN55064.2022.9891992
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 3 | 3 | 4 |
Baptista, D., Correia, J., Pereira, B., & Rocha, M. (2022). A comparison of different compound representations for drug sensitivity prediction. In Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021) (pp. 145–154). Springer International Publishing. https://doi.org/10.1007/978-3-030-86258-9_15
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 2 | 1 | 2 |
2021
Sousa, T., Correia, J., Pereira, V., & Rocha, M. (2021b). Generative Deep Learning for targeted compound design. Journal of Chemical Information and Modeling, 61(11), 5343–5361. https://doi.org/10.1021/acs.jcim.0c01496
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 105 | 93 | 162 |
Sousa, T., Correia, J., Pereira, V., & Rocha, M. (2021a). Combining multi-objective evolutionary algorithms with deep generative models towards focused molecular design. In Lecture Notes in Computer Science (pp. 81–96). Springer International Publishing. https://doi.org/10.1007/978-3-030-72699-7_6
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 5 | 5 | 12 |
2019
Correia, J., Resende, T., Baptista, D., & Rocha, M. (2020). Artificial intelligence in biological activity prediction. In Practical Applications of Computational Biology and Bioinformatics, 13th International Conference (pp. 164–172). Springer International Publishing. https://doi.org/10.1007/978-3-030-23873-5_20
| Source | Scopus | Web of Science | Google Scholar |
|---|---|---|---|
| Citations | 6 | 4 | 11 |
Oral Presentations
- Correia, J., Pereira, V., & Rocha, M. (2023). Combining evolutionary algorithms with reaction rules towards focused molecular design. Talk presented at the Genetic and Evolutionary Computation Conference (GECCO) 2023 (Remote), Lisbon, July 15-19 2023
- Correia, J., Capela, J., Pereira, V., & Rocha, M. (2023). DeepMol: a python-based machine and deep learning framework for drug discovery. Poster presented at the XII Bioinformatics Open Days (BOD 2023), University of Minho, Braga, Portugal, March 16-18 2023
- Correia, J., Carreira, R., Pereira, V., & Rocha, M. (2022). Predicting the number of biochemical transformations needed to synthesize a compound. Talk presented at the International Joint Conference on Neural Networks, IJCNN 2022, Padua, Italy, July 18-23, 2022
- Correia, J., Resende, T., Baptista, D., & Rocha, M. (2020). Artificial intelligence in biological activity prediction. Talk presented at the Practical Applications of Computational Biology and Bioinformatics (PACBB 2019), 13th International Conference, Ávila, Spain, June 26-28 2019
- Correia, J., & Rocha, M. (2019). Development of machine learning-based tools for the selection and design of chemical compounds Invited talk presented at the Data Science Portugal meeting #64, University of Minho, Braga, Portugal, November 21 2019
