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TYC Highlight Seminar: Enhanced sampling simulations in drug design – Alessio Lodola, University of Parma
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Meeting ID: 913 7918 6134
20 July 2023 @ 3:00 pm – 5:00 pm
Alessio Lodola, Department of Food & Drug, University of Parma, Italy; firstname.lastname@example.orgLODOLA Alessio | Teaching staff (unipr.it)
Enhanced sampling methods are becoming increasingly important in modern computational medicinal chemistry, flanking standard approaches such as protein-ligand docking and molecular dynamics (MD). This is due to the impressive progresses made with GPU cards in term of performance as well as with the development of improved codes. These progresses are making possible the exhaustive exploration of relevant degrees of freedom of protein-ligand complexes, with the possibility to simulate unbinding/binding events, accounting for conformational rearrangements and solvation effects. In these favorable conditions, the reconstruction of the free-energy surface (FES) of binding has become possible also on desk computers in less than a week of calculations, with an accuracy depending on the quality of the potential employed and on the approximations made in the definition of the virtual model under investigation. The FES of binding is of pivotal interest for rational drug design as its knowledge can allow to identify productive binding schemes for a small molecule to its target and to rank different ligands targeting the same receptor on a free–energy scale. In the present talk, I’ll describe our recent experiences regarding the retrospective and perspective application of enhanced sampling in drug design.
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Lodola et al, J Med Chem, 2017, 60, 4304.
Castelli et al, J Med Chem 2020, 63, 1261.
Galvani et al, submitted.
Alessio Lodola received his PhD in medicinal chemistry 2006 working at the University of Parma. After a stint at the University of Bristol (UK) and at the Italian Institute of Technology (IIT) in Genoa, in 2014 Dr. Lodola became Associate Professor at the University of Parma where is currently working.
Dr. Lodola’s research focuses on the use of multiscale simulations for the design of endocannabinoid modulators, inhibitors of kinases, and protein-protein interaction antagonists.