Florbela Pereira is part of the Chemoinformatics lab of LAQV REQUIMTE- FCT-NOVA since 2007. Her main scientific interests include automatic learning of chemical reactivity, the use of machine learning techniques to model Spectra–Structure and Structure-Activity Relationships of natural products/marine natural products and software solutions for the processing of molecular structures and chemical data by blind users. More recently, FP is very interested in the structural elucidation of secondary metabolites from marine actinomycete bacteria and their biological activity evaluation both in real and in virtual screening en route to new drugs. Moreover, Florbela Pereira explores as well a big data approach to the ultra-fast prediction of DFT-calculated properties (e.g. the fast estimation of HOMO and LUMO orbital energies, dipole moments).
Chemoinformatis; Machine Learning; Big Data; Marine Natural Products; Drug Discovery
Florbela Pereira is part of the Chemoinformatics lab of LAQV REQUIMTE- FCT-NOVA since 2007. Her main scientific interests include automatic learning of chemical reactivity, the use of machine learning techniques to model Spectra–Structure and Structure-Activity Relationships of natural products/marine natural products and software solutions for the processing of molecular structures and chemical data by blind users. More recently, FP is very interested in the structural elucidation of secondary metabolites from marine actinomycete bacteria and their biological activity evaluation both in real and in virtual screening en route to new drugs. Moreover, Florbela Pereira explores as well a big data approach to the ultra-fast prediction of DFT-calculated properties (e.g. the fast estimation of HOMO and LUMO orbital energies, dipole moments).
- Tiago Dias; Susana Gaudêncio; Florbela Pereira*. 2019. "A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Staphylococcus aureus Infection Therapy". Marine Drugs 17: 16. https://doi.org/10.3390/md17010016.
- Sara Cruz; Sofia Gomes; Pedro Borralho; Cecília Rodrigues; Susana Gaudêncio; Florbela Pereira*. 2018. "In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs". Biomolecules 8: 56. https://doi.org/10.3390/biom8030056.
- Gilberto Pereira; Beatriz Szwarc; Miguel A. Mondragão; Pedro A. Lima;Florbela Pereira*. 2018. "A Ligand-Based Approach to the Discovery of Lead-Like Potassium Channel KV 1.3 Inhibitors". ChemistrySelect 3 (5): 1352-1364. http://dx.doi.org/10.1002/slct.201702977.
-Florbela Pereira; João Aires-de-Sousa*. 2018. "Machine learning for the prediction of molecular dipole moments obtained by density functional theory". Journal of Cheminformatics 10. https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0296-5.
- Florbela Pereira; Kaixia Xiao; Diogo A. R. S. Latino; Chengcheng Wu; Qingyou Zhang*; João Aires-de-Sousa*. 2017. "Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals". Journal of Chemical Information and Modeling 57 (1): 11-21. http://dx.doi.org/10.1021/acs.jcim.6b00340.
- Yuri Binev; Daniela Peixoto; Florbela Pereira; Ian Rodrigues; Sofia Cavaco; Ana M Lobo; João Aires-de-Sousa. 2018. "NavMol 3.0: enabling the representation of metabolic reactions by blind users". Bioinformatics 34 (1): 120-121. http://dx.doi.org/10.1093/bioinformatics/btx556.