Antibodies are specialized proteins used by the immune system to eliminate unrecognized, potentially harmful molecules (antigens). Their ability to bind antigens with high specificity makes them an ideal molecule to work with in pharmaceutical research and drug development. However modelling antibodies offers endless challenges from a structural perspective, which have been only partially addressed by the recent developments in machine learning-based structure prediction (e.g. AlphaFold21, AlphaFold32). These algorithms tend to rely on coevolutionary information, which is missing in both the antibody’s Complementarity-Determining Regions (CDRs) and between the antibody and antigen sequences. In this talk I will discuss when and how it is possible to obtain accurate structural predictions of these proteins and their complexes. I will demonstrate how integrating experimental data with AI-driven modeling within the BioExcel flagship software HADDOCK3-4 improves prediction accuracy5. Such models can then be used as a starting point for improving the binding properties of the complexes through antibody design. I will showcase real-world examples of antibody structural prediction challenges, focusing on cases where pure machine learning-based prediction is unsuccessful. References Highly accurate protein structure prediction with AlphaFold J Jumper et al Nature 596 (7873), 583-589 Accurate structure prediction of biomolecular interactions with AlphaFold 3. J Abramson et al Nature 630.8016 (2024): 493-500 HADDOCK: a protein-protein docking approach based on biochemical or biophysical information C Dominguez, R Boelens, AMJJ Bonvin Journal of the American Chemical Society 125 (7), 1731-1737 The HADDOCK2.4 Web Server: A Leap Forward in Integrative Modelling of Biomolecular Complexes. Honorato, R. V. et al. Nature protocols 19 (11), 3219-3241 Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking M Giulini et al Bioinformatics 40 (10), btae583