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Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. We thoroughly assessed AlphaFold-Multimer accuracy for structure prediction of interactions involving folded domains binding to short linear motifs from the ELM database. The structure predictions were highly sensitive but not very specific when using small protein fragments. Sensitivity decreased substantially when using long protein fragments or full length proteins with intrinsically disordered regions. We delineated a fragmentation strategy to optimize sensitivity and applied it to interactions between proteins associated with neurodevelopmental disorders. This enabled prediction of highly confident and likely disease-related novel interfaces, but also resulted in many high scoring false positive predictions. Experiments supported predicted interfaces between CREBZF-HCFC1, FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.