Machine Learning in Structural Bioinformatics
Macromolecules such as proteins, DNA, and RNA are the key structures responsible for the "living" property of all living systems. Several decades of experimental methodology development resulted in a huge amount of 3D structural data available for these molecules. However, not everything can be catched experimentally, and, together with traditional simulations, machine learning approaches are widely used now to predict molecular interactions, find drug candidates and get insights on macromolecules structure and functionality. We are going to discuss the current challenges in structural bioinformatics and cheminformatics, data and feature extraction problems, and existing algorithms from linear regression to graph CNNs.
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