Coumarins as modulators of protein–protein interactions: Computational insights, biological mechanisms, and translational perspectives

Coumarins are versatile bioactive scaffolds capable of interacting with diverse biomolecules, yet their potential role in modulating protein–protein complexes remains insufficiently characterized. Modern computational biology now enables rational exploration of how small molecules can influence structural coupling, signal transduction, and pathway rewiring at the interface of proteins. This review aims to synthesize emerging evidence on how integrated computational workflows—combining docking, molecular dynamics, network analysis, and machine-learning—can be leveraged to understand and predict coumarin-driven modulation of protein–protein interactions. Published computational pipelines were surveyed for their application to coumarin–protein complexes, including virtual screening, binding free-energy estimation, structural network modeling, and experimental cross-validation approaches. Mechanistic insights from case studies in signaling, transcriptional regulation, and enzyme assemblies were collated to illustrate biological relevance. Published computational studies suggest that coumarins may engage protein interfaces or PPI-associated regulatory sites through orthosteric or allosteric binding, with predicted consequences for protein conformation and pathway behavior. Across the surveyed literature, simulations predicted ligand-dependent changes involving signaling hubs such as MAPK and JAK/STAT, whereas network-based studies proposed broader changes in interaction landscapes. The strongest evidence derives from selected coumarin-based modulators of the BubR1–CENP-E and LXRα–NCOR1/SMRT complexes, for which computational predictions are supported by biophysical and cellular validation. Unlike conventional reviews that discuss individual computational techniques separately, this review integrates molecular docking, molecular dynamics simulations, network analysis, and machine-learning into a unified computational workflow that facilitates systematic target prioritization, mechanistic interpretation, and rational optimization of coumarin-based protein–protein interaction modulators for subsequent experimental validation. Computational biology provides a powerful lens for unraveling how coumarins influence protein–protein complex behavior at multiple organizational layers. While prediction uncertainties persist, integrative workflows linking structural modeling with systems-level validation offer a promising avenue for transforming coumarins into next-generation chemical probes or therapeutic candidates. Future refinement of data resources, cross-omics integration, and phenotypic readouts may accelerate the translation of in silico predictions into biologically meaningful outcomes.