Human Snk is an evolutionarily conserved serine/threonine kinase essential for the maintenance of endocrine stability. The protein consists of a N-terminal catalytic domain and a C-terminal polo-box domain (PBD) that determines subcellular localization and substrate specificity. Here, an integrated strategy is described to explore the vast structural diversity space of Snk PBD-binding phosphopeptides at a molecular level using machine learning modeling, annealing optimization, dynamics simulation, and energetics rescoring, focusing on the recognition specificity and motif preference of the Snk PBD domain. We further performed a systematic rational design of potent phosphopeptide ligands for the domain based on the harvested knowledge, from which a few potent binders were also confirmed by fluorescence-based assays. A phosphopeptide PP17 was designed as a good binder with affinity improvement by 6.7-fold relative to the control PP0, while the other three designed phosphopeptides PP7, PP13, and PP15 exhibit a comparable potency with PP0. In addition, a basic recognition motif that divides potent Snk PBD-binding sequences into four residue blocks was defined, namely [Χ-5Χ-4]block1-[Ω-3Ω-2Ω-1]block2-[pS0/pT0]block3-[Ψ+1]block4, where the X represents any amino acid, Ω indicates polar amino acid, Ψ denotes hydrophobic amino acid, and pS0/pT0 is the anchor phosphoserine/phosphothreonine at reference residue position 0.