O-6-methylguanine-DNA　methyltransferase　(MGMT),　a　unique　DNA　repair　enzyme,　can　confer　resistance　to　DNA　anticancer　alkylating　agents　that　modify　the　O-6-position　of　guanine.　Thus,　inhibition　of　MGMT　activity　in　tumors　has　a　great　interest　for　cancer　researchers　because　it　can　significantly　improve　the　anticancer　efficacy　of　such　alkylating　agents.　In　this　study,　we　performed　a　quantitative　structure　activity　relationship　(QSAR)　and　classification　study　based　on　a　total　of　134　base　analogs　related　to　their　ED50　values　(50%　inhibitory　concentration)　against　MGMT.　Molecular　information　of　all　compounds　were　described　by　quantum　chemical　descriptors　and　Dragon　descriptors.　Genetic　algorithm　(GA)　and　multiple　linear　regression　(MLR)　analysis　were　combined　to　develop　QSAR　models.　Classification　models　were　generated　by　seven　machine-learning　methods　based　on　six　types　of　molecular　fingerprints.　Performances　of　all　developed　models　were　assessed　by　internal　and　external　validation　techniques.　The　best　QSAR　model　was　obtained　with　Q(Loo)(2)　=　0.83,　R-2　=　0.87,　Q(ext)(2)　=　0.67,　and　R-ext(2)　=　0.69　based　on　84　compounds.　The　results　from　QSAR　studies　indicated　topological　charge　indices,　polarizability,　ionization　potential　(IP),　and　number　of　primary　aromatic　amines　are　main　contributors　for　MGMT　inhibition　of　base　analogs.　For　classification　studies,　the　accuracies　of　10-fold　cross-validation　ranged　from　0.750　to　0.885　for　top　ten　models.　The　range　of　accuracy　for　the　external　test　set　ranged　from　0.800　to　0.880　except　for　PubChem-Tree　model,　suggesting　a　satisfactory　predictive　ability.　Three　models　(Ext-SVM,　Ext-Tree　and　Graph-RF)　showed　high　and　reliable　predictive　accuracy　for　both　training　and　external　test　sets.　In　addition,　several　representative　substructures　for　characterizing　MGMT　inhibitors　were　identified　by　information　gain　and　substructure　frequency　analysis　method.　Our　studies　might　be　useful　for　further　study　to　design　and　rapidly　identify　potential　MGMT　inhibitors.