The　quantitative　structure-activity　relationship　(QSAR)　studies　are　investigated　in　a　series　of　chloroethylnitrosoureas　(CENUs)　acting　as　alkylating　agents　of　tumors　by　neural　networks　(NNs).　The　QSAR　model　is　described　inaccurately　by　the　traditional　multiple　linear　regression　(MLR)　model　for　the　substitution　of　CENUs　at　N-3,　whose　characteristics　play　key　roles　in　the　biological　activity.　A　nonlinear　QSAR　study　is　undertaken　by　a　three-layered　NN　model,　using　molecular　descriptors　that　are　known　to　be　responsible　for　the　antitumor　activity　to　optimize　the　input　variables　of　the　MLR　model.　The　results　demonstrate　that　NN　models　present　the　relationship　between　antitumor　activity　and　molecular　descriptors　clearly,　and　they　yield　predictions　in　excellent　agreement　with　the　experiment＇s　obtained　values　(R(2)　=　0.983).　The　R(2)　value　is　0.983　for　the　5-8-1　NN　model,　compared　with　0.506　for　the　MLR　model,　and　the　nonlinear　model　is　able　to　account　for　98.3%　of　the　variance　of　antitumor　activities.　(C)　2011　Elsevier　Inc.　All　rights　reserved.