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In last years we have seen a technological transition in drug discovery, in which the serendipitous approach has been largely replaced by rational drug design. The sequencing of a human genome has unveiled a great number of potentially interesting drug targets; on the other hand, computer sciences have contributed fast hardware and computing solutions. Rational drug design combines knowledge and skills from multiple fields such as chemoinformatics and molecular modeling. Our group implements innovative algorithms and softwares for molecular design as well hit and lead finding and optimization. In particular, we investigate structure- and ligand-based approaches for virtual screening aiming at the identification of novel active compounds. These approaches include implementation of machine learning and data mining algorithm to systematically perform pattern extraction and to explore ligand-target interactions. Our research interests also cover in silico polypharmacology. A ligand might interact with many targets, and a target may accommodate different types of ligands. The amount of high-quality experimental data allow us to address more complex questions involving multiple ligands, multiple binding sites and multiple receptor molecules. The integration of these data in a network framework have provided new insights of molecular basis of complex diseases and have enabled a network-based view of systems biology. Polypharmacology and the design of innovative drugs that affect the proper targets in a disease biological network have been claimed as a new paradigm in drug discovery. Recent Publications
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