Responsible: Dr. Miguel X. Fernandes
In an effort to enhance our PDD strategy, we have envisioned the use of computational methods in combination with the data obtained from the phenotypic screen. Noteworthy, computational methods have played a major role in the development of therapeutically important small-molecules in the last decades. We have started the development of artificial neural networks modelling to correlate the chemical structure with the experimental data of the phenotypic assays in order to allow the prediction of activity, to run virtual screenings, or to anticipate pharmacokinetic and pharmacodynamics. We also use docking experiments to gain a better understanding on how small-molecules might bind to the cellular target.
We work on different approaches to correlate the experimental bioactivity data with the molecular descriptors of the analyzed compounds. We highlight QSAR studies, artificial neuron networks, molecular modeling to explain ligand-receptor interactions, and reverse molecular modeling.