Artificial Intelligence & Deep Learning Approaches for Structural Bioinformatics

Dear Colleagues,

Deep learning methods have revolutionized the field of machine learning since their rapid development in 2012. These methods have shown spectacular performances in fields as varied as digital image recognition and interpretation, natural language processing, and Go gaming. These approaches have made it possible to envisage the treatment of complex bioinformatics problems, especially in the field of structural bioinformatics. Deep learning methods open up new avenues of research, to deal with problems which up until recently were still considered too complex or fields which were no longer progressing. Recently, problems as varied as prediction of secondary structures, model quality assessment, and structure prediction determined from co-evolutive constraints have demonstrated striking improvement thanks to deep learning approaches. In this Special Issue, we propose a broad overview of recent advances in various fields of structural bioinformatics that have benefited from the contributions of Deep Learning and Artificial Intelligence. Potential topics include, but are not limited to, the following:

• Structural Bioinformatics 1: Local structure prediction (secondary structure, local conformation & phi & psi angle, accessibility)
• Structural Bioinformatics 2: Global structure prediction (Co-evolution contact map)
• Structural Bioinformatics 3: Structural model assessment
• Protein/Protein interactions
• Drug discovery and drug design
• Protein function and modification predictions
• Computational protein design
• Antibodies design and prediction

Prof. Dr. Alexandre G. de Brevern
Prof. Jean-Christophe Gelly
Guest Editors

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