Abstract Protein–protein interactions (PPIs) regulate essential cellular processes and represent an important class of therapeutic targets; however, discovering effective modulators of PPIs remains a formidable challenge. Although deep learning approaches have been widely explored for PPI modulator discovery, many rely on simplified representations that obscure interchain boundary information and fine-grained PPI–modulator interaction (PPIMI) patterns, limiting their robustness under distribution shifts. To address this challenge, we introduce TvTPPIMI, a framework that leverages learnable boundary tokens to encode partner-aware boundary information and models PPIMI at atom–residue resolution. In a case study targeting the AURKA–TPX2 interaction, TvTPPIMI prioritized putative modulatory candidates from a small-molecule screening library. Structure-based docking, attention analysis, multireplica molecular dynamics simulations, MM/GBSA binding free-energy estimation, and noncovalent interaction analyses provided post hoc physical support for the stable AURKA binding of selected candidates, highlighting CE02–6266 as the most favorable compound among the tested hits. Together, these results suggest that TvTPPIMI provides a generalizable computational framework with coarse-grained, attention-based interpretive cues for PPIMI prediction and can be integrated with structure- and dynamics-based analyses to support PPI modulator discovery.
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