Dual-specific　tyrosine　phosphorylation　regulated　kinase　1　(DYRK1A)　has　been　regarded　as　a　potential　therapeutic　target　of　neurodegenerative　diseases,　and　considerable　progress　has　been　made　in　the　discovery　of　DYRK1A　inhibitors.　Identification　of　pharmacophoric　fragments　provides　valuable　information　for　structure-　and　fragment-based　design　of　potent　and　selective　DYRK1A　inhibitors.　In　this　study,　seven　machine　learning　methods　along　with　five　molecular　fingerprints　were　employed　to　develop　qualitative　classification　models　of　DYRK1A　inhibitors,　which　were　evaluated　by　cross-validation,　test　set,　and　external　validation　set　with　four　performance　indicators　of　predictive　classification　accuracy　(CA),　the　area　under　receiver　operating　characteristic　(AUC),　Matthews　correlation　coefficient　(MCC),　and　balanced　accuracy　(BA).　The　PubChem　fingerprint-support　vector　machine　model　(CA　=　0.909,　AUC　=　0.933,　MCC　=　0.717,　BA　=　0.855)　and　PubChem　fingerprint　along　with　the　artificial　neural　model　(CA　=　0.862,　AUC　=　0.911,　MCC　=　0.705,　BA　=　0.870)　were　considered　as　the　optimal　modes　for　training　set　and　test　set,　respectively.　A　hybrid　data　balancing　method　SMOTETL,　a　combination　of　synthetic　minority　over-sampling　technique　(SMOTE)　and　Tomek　link　(TL)　algorithms,　was　applied　to　explore　the　impact　of　balanced　learning　on　the　performance　of　models.　Based　on　the　frequency　analysis　and　information　gain,　pharmacophoric　fragments　related　to　DYRK1A　inhibition　were　also　identified.　All　the　results　will　provide　theoretical　supports　and　clues　for　the　screening　and　design　of　novel　DYRK1A　inhibitors.