Nitroaromatic　compounds　(NACs)　are　an　important　type　of　environmental　organic　pollutants.　However,　it　is　lack　of　sufficient　information　relating　to　their　potential　adverse　effects　on　human　health　and　the　environment　due　to　the　limited　resources.　Thus,　using　in　silico　technologies　to　assess　their　potential　hazardous　effects　is　urgent　and　promising.　In　this　study,　quantitative　structure　activity　relationship　(QSAR)　and　classification　models　were　constructed　using　a　set　of　NACs　based　on　their　mutagenicity　against　Salmonella　typhimurium　TA100　strain.　For　QSAR　studies,　DRAGON　descriptors　together　with　quantum　chemistry　descriptors　were　calculated　for　characterizing　the　detailed　molecular　information.　Based　on　genetic　algorithm　(GA)　and　multiple　linear　regression　(MLR)　analyses,　we　screened　descriptors　and　developed　QSAR　models.　For　classification　studies,　seven　machine　learning　methods　along　with　six　molecular　fingerprints　were　applied　to　develop　qualitative　classification　models.　The　goodness　of　fitting,　reliability,　robustness　and　predictive　performance　of　all　developed　models　were　measured　by　rigorous　statistical　validation　criteria,　then　the　best　QSAR　and　classification　models　were　chosen.　Moreover,　the　QSAR　models　with　quantum　chemistry　descriptors　were　compared　to　that　without　quantum　chemistry　descriptors　and　previously　reported　models.　Notably,　we　also　obtained　some　specific　molecular　properties　or　privileged　substructures　responsible　for　the　high　mutagenicity　of　NACs.　Overall,　the　developed　QSAR　and　classification　models　can　be　utilized　as　potential　tools　for　rapidly　predicting　the　mutagenicity　of　new　or　untested　NACs　for　environmental　hazard　assessment　and　regulatory　purposes,　and　may　provide　insights　into　the　in　vivo　toxicity　mechanisms　of　NACs　and　related　compounds.