Unlike traditional 'one-to-many' approaches, ICDMO's digital library-to-library screening uses computational deep learning to evaluate every possible pairwise interaction between two protein libraries (bait library A × prey library B). The platform combines MEGADOCK for initial high-throughput docking with AI-PPI for precise secondary analysis, narrowing millions of candidate pairs to high-confidence interaction networks. Ready-to-use whole-genome digital libraries covering 100+ species and transcription factor libraries for 20+ species are available for immediate deployment — zero experimental cost, results in days.
The platform performs a "many-to-many" interaction screen by: (1) Modeling 3D protein structures for every gene in both libraries using AI-based structure prediction; (2) Running MEGADOCK all-to-all protein docking to generate initial interaction scores; (3) Applying the AI-PPI deep learning model for high-precision secondary analysis; (4) Delivering ranked interaction pairs with gene IDs, modeling scores, and binary interaction calls (1 = predicted interaction, 0 = no interaction), sorted by descending score. The total interaction score represents the predicted docking score between two proteins — higher score indicates greater likelihood of interaction.
Note: All services are for research use only and not intended for diagnostic or therapeutic purposes.
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