Faculdade

Departamento

Florbela Pereira

Investigadora Auxiliar
Secção de Química
128
(+351) 212948300
10994
florbela.pereira@fct.unl.pt

My main scientific interests include automatic learning of chemical reactivity, the use of machine learning techniques to predict by modelling the 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, I am 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 drug discovery. Moreover, I explore a big data approach as well to the ultra-fast prediction of DFT-calculated properties (e.g. the fast estimation of HOMO and LUMO orbital energies, dipole moments, ionization potential and electron affinity energies). Computer-aided drug design (CADD) approaches using QSAR modelling and molecular docking simulations have been one of my main topics of work. 

Áreas de Investigação

Chemoinformatis; Machine Learning; Big Data; Marine Natural Products; Drug Discovery

Research scholarship for PhD student or person holding a master degree - 2023 Call for PhD Research Scholarships
It is open one (1) public competition for the assignment of one (1) Research Scholarship for PhD student or persons holding a Master in Chemoinformatics Lab, Chemistry Department, Faculty of Science and Technology, Nova.
1. Scientific Areas: i. Chemoinformatics; ii. Big Data; iii. Machine Learning; iv. Marine Natural Products (MNP); v. Nuclear Magnetic Resonance (NMR); vi. Drug Discovery.
2. Work Plan: i. A chemoinformatics exploration of spectral and quantum chemistry patterns for the discovery of new drug leads from marine natural products.
Superviser: Dr. Florbela Pereira (FCT-Nova, florbela.pereira@fct.unl.pt)
The selected candidate will be apply to the 2020 Call for PhD Research Scholarships (https://www.fct.pt/apoios/bolsas/concursos/individuais2020.phtml.en).

Interesses Científicos

My main scientific interests include automatic learning of chemical reactivity, the use of machine learning techniques to predict by modelling the 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, I am 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 drug discovery. Moreover, I explore a big data approach as well to the ultra-fast prediction of DFT-calculated properties (e.g. the fast estimation of HOMO and LUMO orbital energies, dipole moments, ionization potential and electron affinity energies). Computer-aided drug design (CADD) approaches using QSAR modelling and molecular docking simulations have been one of my main topics of work.




Publicações Representativas

- Letícia D. Costa, Carlos F.M. Silva*, Diana C.G.A. Pinto, Artur M.S. Silva, Florbela Pereira*, Maria Amparo F. Faustino, Augusto C. Tomé*. 2023."Discovery of thiazolo[5,4-c]isoquinoline based compounds as acetylcholinesterase inhibitors through computational target prediction, molecular docking and bioassay".
J. Mol. Struct.,2023, 1291,136088. https://doi.org/10.1016/j.molstruc.2023.136088.

- Alaa M. Elgohary, Abdo A. Elfiky, Florbela Pereira, Tarek Mohamed, Abd El-Aziz, Mansour Sobeh, Reem K. Arafa, AmrEl-Demerdash*. 2022. "Investigating the structure-activity relationship of marine polycyclic batzelladine alkaloids as promising inhibitors for SARS-CoV-2 main protease (Mpro)". Comput. Biol. Med. 2022, 147, 105738. https://www.sciencedirect.com/science/article/pii/S0010482522005145.

- Rui P.S.Patrício, Paula A. Videira, Florbela Pereira*.2022."A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy". Bioorg. Med. Chem. 2022, 53, 116530. https://www.sciencedirect.com/science/article/pii/S0968089621005381.

- Rafael Mamede, Florbela Pereira, João Aires-de-Sousa*.2021."Machine learning prediction of UV–Vis spectra features of organic compounds related to photoreactive potential". Sci. Rep. 2021, 11, 23720. https://www.nature.com/articles/s41598-021-03070-9.
doi: s41598-021-03070-9.

- Florbela Pereira*.2021. "Machine Learning Methods to Predict the Terrestrial and Marine Origin of Natural Products".Mol. Inf. 2021, 40, 2060034. https://onlinelibrary.wiley.com/doi/full/10.1002/minf.202060034.

-Susana P. Gaudêncio and Florbela Pereira*. 2020."A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition". Mar. Drugs 2020, 18, 6333.772 (2019). https://www.mdpi.com/1660-3397/18/12/633

 

-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.


- Florbela Pereira*. 2023."Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory" ChemistrySelect, 2023, 8, e202300036. https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/slct.202300036.


- 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.

Websites

LAQV REQUIMTE

ORCID

Researcher ID

Researchgate