All　areas　of　the　modern　society　are　affected　by　fluorine　chemistry.　In　particular,　fluorine　plays　an　important　role　in　medical,　pharmaceutical　and　agrochemical　sciences.　Amongst　various　fluoro-organic　compounds,　trifluoromethyl　(CF3)　group　is　valuable　in　applications　such　as　pharmaceuticals,　agrochemicals　and　industrial　chemicals.　In　the　present　study,　following　the　strict　OECD　modelling　principles,　a　quantitative　structure-toxicity　relationship　(QSTR)　modelling　for　the　rat　acute　oral　toxicity　of　trifluoromethyl　compounds　(TFMs)　was　established　by　genetic　algorithm-multiple　linear　regression　(GA-MLR)　approach.　All　developed　models　were　evaluated　by　various　state-of-the-art　validation　metrics　and　the　OECD　principles.　The　best　QSTR　model　included　nine　easily　interpretable　2D　molecular　descriptors　with　clear　physical　and　chemical　significance.　The　mechanistic　interpretation　showed　that　the　atom-type　electro-topological　state　indices,　molecular　connectivity,　ionization　potential,　lipophilicity　and　some　autocorrelation　coefficients　are　the　main　factors　contributing　to　the　acute　oral　toxicity　of　TFMs　against　rats.　To　validate　that　the　selected　2D　descriptors　can　effectively　characterize　the　toxicity,　we　performed　the　chemical　read-across　analysis.　We　also　compared　the　best　QSTR　model　with　public　OPERA　tool　to　demonstrate　the　reliability　of　the　predictions.　To　further　improve　the　prediction　range　of　the　QSTR　model,　we　performed　the　consensus　modelling.　Finally,　the　optimum　QSTR　model　was　utilized　to　predict　a　true　external　set　containing　many　untested/unknown　TFMs　for　the　first　time.　Overall,　the　developed　model　contributes　to　a　more　comprehensive　safety　assessment　approach　for　novel　CF3-containing　pharmaceuticals　or　chemicals,　reducing　unnecessary　chemical　synthesis　whilst　saving　the　development　cost　of　new　drugs.