The development of rapid methods to explain and predict peptide interactions, aggregation, and self-assembly has become important to understanding amyloid disease pathology, the shelf stability of peptide therapeutics, and the design of novel peptide materials. Although experimental aggregation databases have been used to develop correlative and statistical models, molecular simulations offer atomic-level details that potentially provide greater physical insight and allow one to single out the most explanatory simple models. Here, we outline one such approach using a case study that develops homodimerization models for serine-glycine peptides with various hydrophobic leucine mutations. Using detailed all-atom simulations, we calculate reference dimerization free energy profiles and binding constants for a small peptide library. We then use statistical methods to systematically assess whether simple interaction models, which do not require expensive simulations and free energy calculation, can capture them. Surprisingly, some combinations of a few simple scaling laws well recapitulate the detailed, all-atom results with high accuracy. Specifically, we find that a recently proposed phenomenological hydrophobic force law and coarse measures of entropic effects in binding offer particularly high explanatory power, underscoring the physical relevance to association that these driving forces can play.