Research group
High Performance Computing in Molecular Modelling
Position
Researcher
Currently, Florbela Pereira (FP) is an Assistant Researcher, individual CEEC, in the Chemoinformatics group (founded in 2002 by Prof. João Aires de Sousa) at LAQV@REQUIMTE, FCT-NOVA and her research focuses on the development of computational methods for drug discovery and materials (e.g. QSAR, QSPR, virtual screening, molecular docking). FP is very interested in machine learning (ML) based discovery from data pre-calculated by DFT methods to predict properties of atoms, bonds, and molecules very quickly and accurately. ML models provide early-stage filters that can identify promising molecules for further screening for materials and drug design.
She has been a part of the Editorial Board for the following Scientific Journals: - Computational and Structural Biotechnology Journal (CSBJ) since 2011; - In silico Methods and Artificial Intelligence for Drug Discovery since 2021 (Frontiers in Drug Discovery); and - Editorial Board Member for section 'Synthesis and Medicinal Chemistry of Marine Natural Products' of Marine Drugs since 2022.
Apart from researching, she has been lecturing at FCT-NOVA since 2009 for the first university cycle.
Representative Publications
Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals
10.1021/acs.jcim.6b00340
In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs
10.3390/biom8030056
Machine learning for the prediction of molecular dipole moments obtained by density functional theory
10.1186/s13321-018-0296-5
Machine Learning Methods to Predict the Terrestrial and Marine Origin of Natural Products
10.1002/minf.202060034
A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy
10.1016/j.bmc.2021.116530
Machine learning prediction of UV-Vis spectra features of organic compounds related to photoreactive potential
10.1038/s41598-021-03070-9
Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach
10.3390/md20020129
A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Staphylococcus aureus Infection Therapy.
10.3390/md17010016
Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
10.1002/slct.202300036
