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Workshop: AI for Science: from Theory to Practice

Modelling biology in novel ways - an AI-first course in Structural Bioinformatics

Kieran Didi · Charles Harris · Pietro LiĆ³ · Rainer Beck


In recent years, there has been tremendous progress in applying data-driven methodologies to study biological questions. The rapidly evolving field of machine learning has gained a plethora of methods that can be applied to structural biology like protein structure prediction. However, the intricacies one faces when analyzing complex biological data are sometimes underappreciated in applications of machine learning methods.On the other hand, biologists often face a language- and method barrier when trying to understand and correctly apply machine learning tools. As a result, they might be using such methods without proper expertise, potentially resulting in incorrect predictions and questionable conclusions about the resulting data.To help remedy these issues, we have developed a holistic 11-unit course in AI-driven Structural Bioinformatics with the aim of (i) encouraging machine learning researchers to learn more about the biological complexity of the data they are analyzing and (ii) allowing biologists to better understand state-of-the-art machine learning algorithms for correct application to biological systems.The course includes video lectures, animated visualisations as well as in-depth exercises and further resources for each of the topics discussed. We hope that our course stimulates collaboration across research communities and lowers the entry barrier for newcomers to understand and investigate structural biology with data-driven tools. Our course is available at \url{}.

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