Knowledge of atomic-level structures of ligand-protein complexes is key for basic research and structure-based drug design. Computational methods have become a valid complement to experiments, but accuracy of predictions generally degrades with the extent of the structural changes associated with binding. Accurate description of ligand flexibility is equally crucial, particularly in virtual screening whereby initial structures are often generated without accounting for structural adaptation in binding. We will discuss the current advances in Computational Biology and machine learning and their synergism in drug resistance and development.
Speakers include: Professor Paolo Ruggerone and Associate Professor Attilio V. Vargiu.
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