| model |
provider |
| PhysChem: Zeta potential NanoXtract model |
NanoSolveIT Tools |
| NanoSolveIT Tool for Assessment of Human Exposure to Nanomaterials |
NanoSolveIT Tools |
| Nanocompound: Toxicity Metal-Oxide: Anantha 2021 |
SbD4nano Nanocompound |
| Nanocompound: Toxicity Metal-Oxide: Gajewicz 2015 |
SbD4nano Nanocompound |
| Nanocompound: LDH(TiO2) |
SbD4nano Nanocompound |
| Nanocompound: LDH(TiO2+ZnO) |
SbD4nano Nanocompound |
| Nanocompound: LDH(TiO2+ZnO) |
SbD4nano Nanocompound |
| Nanocompound: Toxicity Metal-Oxide: Puzyn 2011 |
SbD4nano Nanocompound |
| Nanocompound: Toxicity Metal-Oxide: Serratosa2022 |
SbD4nano Nanocompound |
| Nanocompound: LDH(TiO2): Serratosa2022 |
SbD4nano Nanocompound |
| Nanocompound: LDH(TiO2+ZnO): Serratosa2022 |
SbD4nano Nanocompound |
| Nanocompound: LDH(ZnO): Serratosa2022 |
SbD4nano Nanocompound |
| NanoSolveIT Cytotoxicity (Cell Viability) Prediction for Metal Oxide NPs |
NanoSolveIT Tools |
| DeepDaph |
NanoSolveIT Tools |
| SimpleBMD |
NanoInformaTIX Tools |
| FunMappOne |
NanoSolveIT Tools |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_10 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_11 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_12 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_13 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_14 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_15 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_16 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_17a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_17b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_18 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_19a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_19b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_19c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_19d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_19e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_19f |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_19g |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_19h |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_1a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_1b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_20 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_21 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_22 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_23a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_23b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_24a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_24b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_24c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_24d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_25a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_25b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_25c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_25d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_25e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_25f |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_26 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_27 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_28a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_28b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_29a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_29b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_2a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_2b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_30 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_31a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_31b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_32a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_32b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_33a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_33b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_33c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_33d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_33e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_34a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_34b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_35 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_36 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_37 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38f |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38g |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38h |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38i |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38j |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38k |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38l |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38m |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38n |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38o |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_38p |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_39 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_3a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_3b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_40a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_40b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_41 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_42 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_43 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_44a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_44b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_45 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_46 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_47 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_48 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_49a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_49b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_49c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_49d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_49e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_4a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_4b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_5 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_50a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_50b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_50c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_50d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_50e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_50f |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_51a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_51b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_52a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_52b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_52c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53f |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53g |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53h |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53i |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53j |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53k |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53l |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53m |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53n |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53o |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53p |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53q |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53r |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_53s |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_54a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_54b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_55a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_55b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_55c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_55d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_56 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_57 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_58 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_59a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_59b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_6 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_60 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_61a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_61b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_61c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62f |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62g |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62h |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62i |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_62j |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_63a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_63b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_63c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_64a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_64b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_65 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_66 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_67 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_68a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_68b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_69 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_7 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_70a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_70b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_71 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_72 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_73 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_74 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_75a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_75b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_75c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_75d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_76 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_77a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_77b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_77c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_77d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_78a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_78b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_79 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_8 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_80a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_80b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_81 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_82 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_83 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_84 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_85 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_86 |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87a |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87b |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87c |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87d |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87e |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87f |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87g |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87h |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87i |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87j |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87k |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_87l |
Computational models for the assessment of manufactured nanomaterials |
| https://h2020-sbd4nano.github.io/sbd-data-landscape/Model_9 |
Computational models for the assessment of manufactured nanomaterials |
| Binding to the picrotoxin site of ionotropic GABA receptors leading to epileptic seizures in adult brain |
AOP-Wiki AOPs |
| Cyclooxygenase inhibition leading to reproductive dysfunction via inhibition of female spawning behavior |
AOP-Wiki AOPs |
| Cyclooxygenase inhibition leading to reproductive dysfunction via inhibition of pheromone release |
AOP-Wiki AOPs |
| Cyclooxygenase inhibition leading to reproductive dysfunction via interference with meiotic prophase I /metaphase I transition |
AOP-Wiki AOPs |
| Cyclooxygenase inhibition leading to reproductive dysfunction via interference with spindle assembly checkpoint |
AOP-Wiki AOPs |
| Altered ion channel activity leading impaired heart function |
AOP-Wiki AOPs |
| Chemical binding to tubulin in oocytes leading to aneuploid offspring |
AOP-Wiki AOPs |
| Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas in the mouse and the rat |
AOP-Wiki AOPs |
| Inhibition of iodide pump activity leading to follicular cell adenomas and carcinomas (in rat and mouse) |
AOP-Wiki AOPs |
| Glutamate-gated chloride channel activation leading to acute mortality |
AOP-Wiki AOPs |
| Inhibition of thyroid peroxidase leading to follicular cell adenomas and carcinomas (in rat and mouse) |
AOP-Wiki AOPs |
| Chronic binding of antagonist to N-methyl-D-aspartate receptors (NMDARs) during brain development leads to neurodegeneration with impairment in learning and memory in aging |
AOP-Wiki AOPs |
| Prolyl hydroxylase inhibition leading to reproductive dysfunction via increased HIF1 heterodimer formation |
AOP-Wiki AOPs |
| Unknown MIE leading to reproductive dysfunction via increased HIF-1alpha transcription |
AOP-Wiki AOPs |
| Chronic binding of antagonist to N-methyl-D-aspartate receptors (NMDARs) during brain development induces impairment of learning and memory abilities |
AOP-Wiki AOPs |
| Aryl hydrocarbon receptor activation leading to uroporphyria |
AOP-Wiki AOPs |
| Sodium Iodide Symporter (NIS) Inhibition and Subsequent Adverse Neurodevelopmental Outcomes in Mammals |
AOP-Wiki AOPs |
| Intracellular Acidification Induced Olfactory Epithelial Injury Leading to Site of Contact Nasal Tumors |
AOP-Wiki AOPs |
| Organic anion transporter (OAT1) inhibition leading to renal failure and mortality |
AOP-Wiki AOPs |
| Alkylation of DNA leading to cancer 1 |
AOP-Wiki AOPs |
| Alkylation of DNA leading to cancer 2 |
AOP-Wiki AOPs |
| Endocytic lysosomal uptake leading to liver fibrosis |
AOP-Wiki AOPs |
| EGFR Activation Leading to Decreased Lung Function |
AOP-Wiki AOPs |
| Peptide Oxidation Leading to Hypertension |
AOP-Wiki AOPs |
| Alkylation of DNA in male pre-meiotic germ cells leading to heritable mutations |
AOP-Wiki AOPs |
| Aryl hydrocarbon receptor activation leading to early life stage mortality, via reduced VEGF |
AOP-Wiki AOPs |
| AhR activation leading to preeclampsia |
AOP-Wiki AOPs |
| Interference with thyroid serum binding protein transthyretin and subsequent adverse human neurodevelopmental toxicity |
AOP-Wiki AOPs |
| Inhibition of Calcineurin Activity Leading to Impaired T-Cell Dependent Antibody Response |
AOP-Wiki AOPs |
| Deiodinase 2 inhibition leading to increased mortality via reduced posterior swim bladder inflation |
AOP-Wiki AOPs |
| Deiodinase 2 inhibition leading to increased mortality via reduced anterior swim bladder inflation |
AOP-Wiki AOPs |
| Deiodinase 1 inhibition leading to increased mortality via reduced posterior swim bladder inflation |
AOP-Wiki AOPs |
| Deiodinase 1 inhibition leading to increased mortality via reduced anterior swim bladder inflation |
AOP-Wiki AOPs |
| Thyroperoxidase inhibition leading to increased mortality via reduced anterior swim bladder inflation |
AOP-Wiki AOPs |
| Acetylcholinesterase inhibition leading to acute mortality |
AOP-Wiki AOPs |
| Ionotropic gamma-aminobutyric acid receptor activation mediated neurotransmission inhibition leading to mortality |
AOP-Wiki AOPs |
| Glutamate-gated chloride channel activation leading to neurotransmission inhibition associated mortality |
AOP-Wiki AOPs |
| Binding of electrophilic chemicals to SH(thiol)-group of proteins and /or to seleno-proteins involved in protection against oxidative stress during brain development leads to impairment of learning and memory |
AOP-Wiki AOPs |
| Substance interaction with the pulmonary resident cell membrane components leading to pulmonary fibrosis |
AOP-Wiki AOPs |
| Thyroperoxidase inhibition leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Sodium Iodide Symporter (NIS) Inhibition leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Cyclooxygenase 1 (COX1) inhibition leading to renal failure and mortality |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation contributes to mitochondrial dysfunction and leads to colony loss/failure |
AOP-Wiki AOPs |
| PPARα activation in utero leading to impaired fertility in males |
AOP-Wiki AOPs |
| unknown MIE leading to renal failure and mortality |
AOP-Wiki AOPs |
| Iodotyrosine deiodinase (IYD) inhibition leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Type I iodothyronine deiodinase (DIO1) inhibition leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Type II iodothyronine deiodinase (DIO2) inhibition leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Type III iodotyrosine deiodinase (DIO3) inhibition leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Pendrin inhibition leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Dual oxidase (DUOX) inhibition leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Hepatic nuclear receptor activation leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| 5-hydroxytryptamine transporter (5-HTT) inhibition leading to population increase |
AOP-Wiki AOPs |
| Volatile Organic Chemicals Activate TRPA1 Receptor to Induce Sensory Pulmonary Irritation |
AOP-Wiki AOPs |
| Estrogen receptor activation leading to breast cancer |
AOP-Wiki AOPs |
| Inhibitor binding to topoisomerase II leading to infant leukaemia |
AOP-Wiki AOPs |
| 5-hydroxytryptamine transporter inhibition leading to decreased reproductive success and population decline |
AOP-Wiki AOPs |
| AOP from chemical insult to cell death |
AOP-Wiki AOPs |
| Histone deacetylase inhibition leading to testicular atrophy |
AOP-Wiki AOPs |
| Inhibition of fatty acid beta oxidation leading to nonalcoholic steatohepatitis (NASH) |
AOP-Wiki AOPs |
| Deposition of energy leading to population decline via DNA strand breaks and follicular atresia |
AOP-Wiki AOPs |
| Inhibition of CYP7B activity leads to decreased reproductive success via decreased locomotor activity |
AOP-Wiki AOPs |
| Inhibition of CYP7B activity leads to decreased reproductive success via decreased sexual behavior |
AOP-Wiki AOPs |
| Cyp2E1 Activation Leading to Liver Cancer |
AOP-Wiki AOPs |
| Serotonin transporter activation to seizure |
AOP-Wiki AOPs |
| Androgen receptor agonism leading to reproductive dysfunction (in repeat-spawning fish) |
AOP-Wiki AOPs |
| NFE2/Nrf2 repression to steatosis |
AOP-Wiki AOPs |
| Substance interaction with lung resident cell membrane components leading to atherosclerosis |
AOP-Wiki AOPs |
| Deposition of energy leading to population decline via DNA strand breaks and oocyte apoptosis |
AOP-Wiki AOPs |
| Reduction in photophosphorylation leading to growth inhibition in aquatic plants |
AOP-Wiki AOPs |
| Aromatase inhibition leading to reproductive dysfunction |
AOP-Wiki AOPs |
| Renal protein alkylation leading to kidney toxicity |
AOP-Wiki AOPs |
| L-type calcium channel blockade leading to heart failure via decrease in cardiac contractility |
AOP-Wiki AOPs |
| Uncoupling of oxidative phosphorylation leading to growth inhibition via decreased cell proliferation |
AOP-Wiki AOPs |
| Uncoupling of oxidative phosphorylation leading to growth inhibition via ATP depletion associated cell death |
AOP-Wiki AOPs |
| Uncoupling of oxidative phosphorylation leading to growth inhibition via increased cytosolic calcium |
AOP-Wiki AOPs |
| Uncoupling of oxidative phosphorylation leading to growth inhibition via decreased Na-K ATPase activity |
AOP-Wiki AOPs |
| Uncoupling of oxidative phosphorylation leading to growth inhibition via glucose depletion |
AOP-Wiki AOPs |
| Uncoupling of oxidative phosphorylation leading to growth inhibition via mitochondrial swelling |
AOP-Wiki AOPs |
| Inhibition of thyroid peroxidase leading to impaired fertility in fish |
AOP-Wiki AOPs |
| Deposition of energy leading to lung cancer |
AOP-Wiki AOPs |
| Mitochondrial complex inhibition leading to liver injury |
AOP-Wiki AOPs |
| Histone deacetylase inhibition leads to impeded craniofacial development |
AOP-Wiki AOPs |
| Histone deacetylase inhibition leads to neural tube defects |
AOP-Wiki AOPs |
| Inhibition of complex I of the electron transport chain leading to chemical induced Fanconi syndrome |
AOP-Wiki AOPs |
| Impaired IL-1R1 signaling leading to Impaired T-Cell Dependent Antibody Response |
AOP-Wiki AOPs |
| Cyclooxygenase inhibition leading reproductive failure |
AOP-Wiki AOPs |
| Acetylcholinesterase Inhibition Leading to Neurodegeneration |
AOP-Wiki AOPs |
| Adverse outcome pathway on photochemical toxicity initiated by light exposure |
AOP-Wiki AOPs |
| Binding of electrophilic chemicals to SH(thiol)-group of proteins and /or to seleno-proteins involved in protection against oxidative stress leads to chronic kidney disease |
AOP-Wiki AOPs |
| Mitochondrial complex III antagonism leading to growth inhibition (1) |
AOP-Wiki AOPs |
| Mitochondrial complex III antagonism leading to growth inhibition (2) |
AOP-Wiki AOPs |
| Inhibition of 17α-hydrolase/C 10,20-lyase (Cyp17A1) activity leads to birth reproductive defects (cryptorchidism) in male (mammals) |
AOP-Wiki AOPs |
| Inhibition of 5α-reductase leading to impaired fecundity in female fish |
AOP-Wiki AOPs |
| Estrogen receptor agonism leading to reproductive dysfunction |
AOP-Wiki AOPs |
| Mitochondrial ATP synthase antagonism leading to growth inhibition (1) |
AOP-Wiki AOPs |
| Mitochondrial ATP synthase antagonism leading to growth inhibition (2) |
AOP-Wiki AOPs |
| Inhibition of tyrosinase leads to decreased population in fish |
AOP-Wiki AOPs |
| Increased DNA damage leading to increased risk of breast cancer |
AOP-Wiki AOPs |
| Increased reactive oxygen and nitrogen species (RONS) leading to increased risk of breast cancer |
AOP-Wiki AOPs |
| Oxidative DNA damage leading to chromosomal aberrations and mutations |
AOP-Wiki AOPs |
| Inhibition of retinaldehyde dehydrogenase leads to population decline |
AOP-Wiki AOPs |
| Increases in cellular reactive oxygen species and chronic reactive oxygen species leading to human treatment-resistant gastric cancer |
AOP-Wiki AOPs |
| Deposition of energy leading to population decline via DNA oxidation and follicular atresia |
AOP-Wiki AOPs |
| Inhibition of the mitochondrial complex I of nigro-striatal neurons leads to parkinsonian motor deficits |
AOP-Wiki AOPs |
| Estrogen receptor antagonism leading to reproductive dysfunction |
AOP-Wiki AOPs |
| Thyroid Receptor Antagonism and Subsequent Adverse Neurodevelopmental Outcomes in Mammals |
AOP-Wiki AOPs |
| Inhibition of Cystathionine Beta synthase leading to impaired the early development of anterior-posterior axis |
AOP-Wiki AOPs |
| Lung surfactant function inhibition leading to decreased lung function |
AOP-Wiki AOPs |
| Frustrated phagocytosis-induced lung cancer |
AOP-Wiki AOPs |
| 5α-reductase inhibition leading to short anogenital distance (AGD) in male (mammalian) offspring |
AOP-Wiki AOPs |
| Androgen receptor (AR) antagonism leading to short anogenital distance (AGD) in male (mammalian) offspring |
AOP-Wiki AOPs |
| Decreased testosterone synthesis leading to short anogenital distance (AGD) in male (mammalian) offspring |
AOP-Wiki AOPs |
| Luteinizing hormone receptor antagonism leading to reproductive dysfunction |
AOP-Wiki AOPs |
| Embryonic Activation of the AHR leading to Reproductive failure, via epigenetic down-regulation of GnRHR |
AOP-Wiki AOPs |
| Deposition of energy leading to population decline via DNA oxidation and oocyte apoptosis |
AOP-Wiki AOPs |
| Acetylcholinesterase Inhibition leading to Acute Mortality via Impaired Coordination & Movement |
AOP-Wiki AOPs |
| Stimulation of TLR7/8 in dendric cells leading to Psoriatic skin disease |
AOP-Wiki AOPs |
| Binding to estrogen receptor (ER)-α in immune cells leading to exacerbation of systemic lupus erythematosus (SLE) |
AOP-Wiki AOPs |
| Inhibition of JAK3 leading to impairment of T-Cell Dependent Antibody Response |
AOP-Wiki AOPs |
| Trypsin inhibition leading to pancreatic acinar cell tumors |
AOP-Wiki AOPs |
| Glucocorticoid Receptor activation leading to hepatic steatosis |
AOP-Wiki AOPs |
| Binding to ACE2 leading to lung fibrosis |
AOP-Wiki AOPs |
| Binding of SARS-CoV-2 to ACE2 receptor leading to acute respiratory distress associated mortality |
AOP-Wiki AOPs |
| Alkylation of DNA leading to reduced sperm count |
AOP-Wiki AOPs |
| PPARalpha Agonism Leading to Decreased Viable Offspring via Decreased 11-Ketotestosterone |
AOP-Wiki AOPs |
| Thermal stress leading to population decline (1) |
AOP-Wiki AOPs |
| Thermal stress leading to population decline (2) |
AOP-Wiki AOPs |
| Thermal stress leading to population decline (3) |
AOP-Wiki AOPs |
| Excessive reactive oxygen species production leading to mortality (1) |
AOP-Wiki AOPs |
| Excessive reactive oxygen species production leading to mortality (2) |
AOP-Wiki AOPs |
| Excessive reactive oxygen species production leading to mortality (3) |
AOP-Wiki AOPs |
| Excessive reactive oxygen species production leading to mortality (4) |
AOP-Wiki AOPs |
| Formation of DNA photoproducts leading to growth inhibition (1) |
AOP-Wiki AOPs |
| Glucocorticoid Receptor Agonism Leading to Impaired Fin Regeneration |
AOP-Wiki AOPs |
| DNA methyltransferase inhibition leading to population decline (1) |
AOP-Wiki AOPs |
| DNA methyltransferase inhibition leading to population decline (2) |
AOP-Wiki AOPs |
| DNA methyltransferase inhibition leading to population decline (3) |
AOP-Wiki AOPs |
| DNA methyltransferase inhibition leading to population decline (4) |
AOP-Wiki AOPs |
| LXR activation leading to hepatic steatosis |
AOP-Wiki AOPs |
| DNA methyltransferase inhibition leading to transgenerational effects (1) |
AOP-Wiki AOPs |
| DNA methyltransferase inhibition leading to transgenerational effects (2) |
AOP-Wiki AOPs |
| S-adenosylmethionine depletion leading to population decline (1) |
AOP-Wiki AOPs |
| S-adenosylmethionine depletion leading to population decline (2) |
AOP-Wiki AOPs |
| Androgen receptor (AR) antagonism leading to nipple retention (NR) in male (mammalian) offspring |
AOP-Wiki AOPs |
| Androgen receptor (AR) antagonism leading to decreased fertility in females |
AOP-Wiki AOPs |
| Aromatase inhibition leads to male-biased sex ratio via impacts on gonad differentiation |
AOP-Wiki AOPs |
| Inhibition of 11β-Hydroxysteroid Dehydrogenase leading to decreased population trajectory |
AOP-Wiki AOPs |
| Inhibition of 11β-hydroxylase leading to decresed population trajectory |
AOP-Wiki AOPs |
| Chitinase inhibition leading to mortality |
AOP-Wiki AOPs |
| Chitobiase inhibition leading to mortality |
AOP-Wiki AOPs |
| Peroxisomal Fatty Acid Beta-Oxidation Inhibition Leading to Steatosis |
AOP-Wiki AOPs |
| Chitin synthase 1 inhibition leading to mortality |
AOP-Wiki AOPs |
| Sulfonylureareceptor binding leading to mortality |
AOP-Wiki AOPs |
| Thyroperoxidase inhibition leading to altered visual function via altered retinal layer structure |
AOP-Wiki AOPs |
| Thyroperoxidase inhibition leading to altered visual function via decreased eye size |
AOP-Wiki AOPs |
| Thyroperoxidase inhibition leading to altered visual function via altered photoreceptor patterning |
AOP-Wiki AOPs |
| Competitive binding to thyroid hormone carrier protein transthyretin (TTR) leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Competitive binding to thyroid hormone carrier protein thyroid binding globulin (TBG) leading to altered amphibian metamorphosis |
AOP-Wiki AOPs |
| Cytochrome oxidase inhibition leading to olfactory nasal lesions |
AOP-Wiki AOPs |
| PPARα activation leading to hepatocellular adenomas and carcinomas in rodents |
AOP-Wiki AOPs |
| Androgen receptor antagonism leading to testicular cancer |
AOP-Wiki AOPs |
| Binding of Sars-CoV-2 spike protein to ACE 2 receptors expressed on brain cells (neuronal and non-neuronal) leads to neuroinflammation resulting in encephalitis |
AOP-Wiki AOPs |
| Androgen receptor agonism leading to male-biased sex ratio |
AOP-Wiki AOPs |
| Dysregulated prolonged Toll Like Receptor 9 (TLR9) activation leading to Multi Organ Failure involving Acute Respiratory Distress Syndrome (ARDS) |
AOP-Wiki AOPs |
| Binding to ACE2 leading to thrombosis and disseminated intravascular coagulation |
AOP-Wiki AOPs |
| Protein Alkylation leading to Liver Fibrosis |
AOP-Wiki AOPs |
| Binding of viral S-glycoprotein to ACE2 receptor leading to dysgeusia |
AOP-Wiki AOPs |
| Inhibition of Angiotensin-converting enzyme 2 leading to liver fibrosis |
AOP-Wiki AOPs |
| Deposition of ionizing energy leading to population decline via inhibition of photosynthesis |
AOP-Wiki AOPs |
| Deposition of ionising energy leading to population decline via mitochondrial dysfunction |
AOP-Wiki AOPs |
| Deposition of ionising energy leading to population decline via programmed cell death |
AOP-Wiki AOPs |
| Oxygen-evolving complex damage leading to population decline via inhibition of photosynthesis |
AOP-Wiki AOPs |
| Covalent Binding, Protein, leading to Increase, Allergic Respiratory Hypersensitivity Response |
AOP-Wiki AOPs |
| Decreased fibrinolysis and activated bradykinin system leading to hyperinflammation |
AOP-Wiki AOPs |
| SARS-CoV-2 infection of olfactory epithelium leading to impaired olfactory function (short-term anosmia) |
AOP-Wiki AOPs |
| Binding of Sars-CoV-2 spike protein to ACE 2 receptors expressed on pericytes leads to disseminated intravascular coagulation resulting in cerebrovascular disease (stroke) |
AOP-Wiki AOPs |
| Bulky DNA adducts leading to mutations |
AOP-Wiki AOPs |
| Inhibition of ALDH1A (RALDH) leading to impaired fertility via disrupted meiotic initiation of fetal oogonia of the ovary |
AOP-Wiki AOPs |
| Inhibition of Fyna leading to increased mortality via decreased eye size (Microphthalmos) |
AOP-Wiki AOPs |
| Ecdysone receptor agonism leading to incomplete ecdysis associated mortality |
AOP-Wiki AOPs |
| Covalent Protein binding leading to Skin Sensitisation |
AOP-Wiki AOPs |
| G protein-coupled estrogen receptor 1 (GPER) signal pathway in the lipid metabolism disrupting effects |
AOP-Wiki AOPs |
| Thyroid peroxidase (TPO) inhibition leads to periventricular heterotopia formation in the developing rat brain |
AOP-Wiki AOPs |
| Organo-Phosphate Chemicals induced inhibition of AChE leading to impaired cognitive function |
AOP-Wiki AOPs |
| SARS-CoV-2 infection leading to hyperinflammation |
AOP-Wiki AOPs |
| SARS-CoV-2 infection leading to pyroptosis |
AOP-Wiki AOPs |
| Frustrated phagocytosis leads to malignant mesothelioma |
AOP-Wiki AOPs |
| Sustained AhR Activation leading to Rodent Liver Tumours |
AOP-Wiki AOPs |
| GSK3beta inactivation leading to increased mortality via defects in developing inner ear |
AOP-Wiki AOPs |
| Oxidative stress Leading to Decreased Lung Function |
AOP-Wiki AOPs |
| Oxidation and antagonism of reduced glutathione leading to mortality via acute renal failure |
AOP-Wiki AOPs |
| Aryl hydrocarbon receptor activation leading to lung fibrosis through IL-6 toxicity pathway |
AOP-Wiki AOPs |
| Aryl hydrocarbon receptor activation leading to lung cancer through IL-6 toxicity pathway |
AOP-Wiki AOPs |
| Aryl hydrocarbon receptor activation leading to lung cancer through AHR-ARNT toxicity pathway |
AOP-Wiki AOPs |
| Aryl hydrocarbon receptor activation leading to impaired lung function through P53 toxicity pathway |
AOP-Wiki AOPs |
| Inhibition of Thyroperoxidase and Subsequent Adverse Neurodevelopmental Outcomes in Mammals |
AOP-Wiki AOPs |
| Toxicological mechanisms of hepatocyte apoptosis through the PARP1 dependent cell death pathway |
AOP-Wiki AOPs |
| Oxidative stress Leading to Decreased Lung Function via CFTR dysfunction |
AOP-Wiki AOPs |
| Oxidative Stress Leading to Decreased Lung Function via Decreased FOXJ1 |
AOP-Wiki AOPs |
| SARS-CoV-2 spike protein binding to ACE2 receptors expressed on pericytes leads to endothelial cell dysfunction, microvascular injury and myocardial infarction. |
AOP-Wiki AOPs |
| ACE2 downregulation following SARS-CoV-2 infection triggers dysregulation of RAAS and can lead to heart failure. |
AOP-Wiki AOPs |
| Disruption of VEGFR Signaling Leading to Developmental Defects |
AOP-Wiki AOPs |
| Binding of SARS-CoV-2 to ACE2 leads to viral infection proliferation |
AOP-Wiki AOPs |
| Deposition of Energy by Ionizing Radiation leading to Acute Myeloid Leukemia |
AOP-Wiki AOPs |
| Deposition of ionising energy leads to population decline via pollen abnormal |
AOP-Wiki AOPs |
| Inhibition of RALDH2 causes reduced all-trans retinoic acid levels, leading to transposition of the great arteries |
AOP-Wiki AOPs |
| Activation of the AhR leading to metastatic breast cancer |
AOP-Wiki AOPs |
| Hypothalamus estrogen receptors activity suppression leading to ovarian cancer via ovarian epithelial cell hyperplasia |
AOP-Wiki AOPs |
| Ionizing radiation-induced DNA damage leads to microcephaly via apoptosis and premature cell differentiation |
AOP-Wiki AOPs |
| Binding to voltage gate sodium channels during development leads to cognitive impairment |
AOP-Wiki AOPs |
| DNA damage and mutations leading to Metastatic Breast Cancer |
AOP-Wiki AOPs |
| Ionizing radiation leads to reduced reproduction in Eisenia fetida via reduced spermatogenesis and cocoon hatchability |
AOP-Wiki AOPs |
| Inhibition of AChE and activation of CYP2E1 leading to sensory axonal peripheral neuropathy and mortality |
AOP-Wiki AOPs |
| Interaction with lung resident cell membrane components leads to lung cancer |
AOP-Wiki AOPs |
| Aryl hydrocarbon receptor activation leading to early life stage mortality via sox9 repression induced impeded craniofacial development |
AOP-Wiki AOPs |
| Aryl hydrocarbon receptor activation leading to early life stage mortality via sox9 repression induced cardiovascular toxicity |
AOP-Wiki AOPs |
| Succinate dehydrogenase inhibition leading to increased insulin resistance through reduction in circulating thyroxine |
AOP-Wiki AOPs |
| AhR activation in the liver leading to Subsequent Adverse Neurodevelopmental Outcomes in Mammals |
AOP-Wiki AOPs |
| AhR activation in the thyroid leading to Subsequent Adverse Neurodevelopmental Outcomes in Mammals |
AOP-Wiki AOPs |
| AFB1: Mutagenic Mode-of-Action leading to Hepatocellular Carcinoma (HCC) |
AOP-Wiki AOPs |
| Antagonism of Smoothened receptor leading to orofacial clefting |
AOP-Wiki AOPs |
| Calcium overload in dopaminergic neurons of the substantia nigra leading to parkinsonian motor deficits |
AOP-Wiki AOPs |
| Binding of SARS-CoV-2 to ACE2 leads to hyperinflammation (via cell death) |
AOP-Wiki AOPs |
| Deposition of energy leads to vascular remodeling |
AOP-Wiki AOPs |
| Various neuronal effects induced by elavl3, sox10, and mbp |
AOP-Wiki AOPs |
| Energy deposition from internalized Ra-226 decay lower oxygen binding capacity of hemocyanin |
AOP-Wiki AOPs |
| Binding of chemicals to ionotropic glutamate receptors leads to impairment of learning and memory via loss of drebrin from dendritic spines of neurons |
AOP-Wiki AOPs |
| Adverse Outcome Pathways diagram related to PBDEs associated male reproductive toxicity |
AOP-Wiki AOPs |
| Androgen receptor (AR) antagonism leading to hypospadias in male offspring |
AOP-Wiki AOPs |
| Deposition of energy leading to occurrence of cataracts |
AOP-Wiki AOPs |
| Binding of agonists to ionotropic glutamate receptors in adult brain causes excitotoxicity that mediates neuronal cell death, contributing to learning and memory impairment. |
AOP-Wiki AOPs |
| Deposition of energy leading to occurrence of bone loss |
AOP-Wiki AOPs |
| Deposition of Energy Leading to Learning and Memory Impairment |
AOP-Wiki AOPs |
| Thyroid hormone antagonism leading to impaired oligodendrocyte maturation during development and subsequent decreased cognition |
AOP-Wiki AOPs |
| Binding to the extracellular protein laminin leading to decreased cognitive function |
AOP-Wiki AOPs |
| Unknown MIE altering cholesterol metabolism leading to decreased cognition |
AOP-Wiki AOPs |
| Increased reactive oxygen species production leading to decreased cognitive function |
AOP-Wiki AOPs |
| Inhibition of voltage-gated sodium channels leading to decreased cognition |
AOP-Wiki AOPs |
| Co-activation of IP3R and RyR leads to socio-economic burden through reduced IQ and non-cholinergic mechanisms |
AOP-Wiki AOPs |
| Decrease, GLI1/2 target gene expression leads to orofacial clefting |
AOP-Wiki AOPs |
| Glutathione conjugation leading to reproductive dysfunction via oxidative stress |
AOP-Wiki AOPs |
| AhR activation leading to liver fibrosis |
AOP-Wiki AOPs |
| Androgen receptor agonism leading to reproduction dysfunction (in zebrafish) |
AOP-Wiki AOPs |
| ERa inactivation alters mitochondrial functions and insulin signalling in skeletal muscle and leads to insulin resistance and metabolic syndrome |
AOP-Wiki AOPs |
| Activation of MEK-ERK1/2 leads to deficits in learning and cognition via disrupted neurotransmitter release |
AOP-Wiki AOPs |
| Activation of MEK-ERK1/2 leads to deficits in learning and cognition via ROS and apoptosis |
AOP-Wiki AOPs |
| Decrease, cholesterol synthesis leads to orofacial clefting |
AOP-Wiki AOPs |
| Reactive Oxygen Species (ROS) formation leads to cancer via inflammation pathway |
AOP-Wiki AOPs |
| Binding of Influenza A Virus (IAV) to Sialic Acid Glycan Receptor leads to viral infection proliferation |
AOP-Wiki AOPs |
| Nrf2 inhibition leading to vascular disrupting effects via inflammation pathway |
AOP-Wiki AOPs |
| Nrf2 inhibition leading to vascular disrupting effects through activating HIF1α, Semaphorin 6A, and Dll4-Notch pathway |
AOP-Wiki AOPs |
| Nrf2 inhibition leading to vascular disrupting effects through activating apoptosis signal pathway and mitochondrial dysfunction |
AOP-Wiki AOPs |
| PPARα activation leading to impaired fertility in adult male rodents |
AOP-Wiki AOPs |
| Demethylation of PPAR promotor leading to vascular disrupting effects |
AOP-Wiki AOPs |
| The AOP framework on ROS-mediated oxidative stress induced vascular disrupting effects |
AOP-Wiki AOPs |
| Reactive Oxygen (ROS) formation leads to cancer via Peroxisome proliferation-activated receptor (PPAR) pathway |
AOP-Wiki AOPs |
| Pregnane X Receptor (PXR) activation leads to liver steatosis |
AOP-Wiki AOPs |
| Liver X Receptor (LXR) activation leads to liver steatosis |
AOP-Wiki AOPs |
| Retinoic acid receptor agonism during neurodevelopment leading to impaired learning and memory |
AOP-Wiki AOPs |
| Essential element imbalance leads to reproductive failure via oxidative stress |
AOP-Wiki AOPs |
| Reduced oligodendrocyte differentiation during neurodevelopment leading to impaired learning and memory |
AOP-Wiki AOPs |
| Decreased, Chicken Ovalbumin Upstream Promoter Transcription Factor II (COUP-TFII) leads to Impaired, Spermatogenesis |
AOP-Wiki AOPs |
| Decreased, Chicken Ovalbumin Upstream Promoter Transcription Factor II (COUP-TFII) leads to Hypospadias, increased |
AOP-Wiki AOPs |
| Decreased Insulin-like peptide 3 (INSL3) leads to Malformation, cryptorchidism - maldescended testis |
AOP-Wiki AOPs |
| Perfluorooctanesulfonic acid (PFOS) binding to peroxisome proliferator-activated receptors (PPARs) causes dysregulation of lipid metabolism and subsequent liver steatosis |
AOP-Wiki AOPs |
| Endocytotic lysosomal uptake leads to intestinal barrier disruption |
AOP-Wiki AOPs |
| Inhibition of Na+/I- symporter (NIS) leads to learning and memory impairment |
AOP-Wiki AOPs |
| AhR activation leading to hepatic steatosis |
AOP-Wiki AOPs |
| NR1I3 (CAR) suppression leading to hepatic steatosis |
AOP-Wiki AOPs |
| Antagonist binding to PPARα leading to body-weight loss |
AOP-Wiki AOPs |
| NR1I2 (Pregnane X Receptor, PXR) activation leading to hepatic steatosis |
AOP-Wiki AOPs |
| NFE2L2/FXR activation leading to hepatic steatosis |
AOP-Wiki AOPs |
| AKT2 activation leading to hepatic steatosis |
AOP-Wiki AOPs |
| Cyclooxygenase inhibition leading to reproductive dysfunction |
AOP-Wiki AOPs |
| Glucocorticoid Receptor (GR) Mediated Adult Leydig Cell Dysfunction Leading to Decreased Male Fertility |
AOP-Wiki AOPs |
| XX Inhibition of Sodium Iodide Symporter and Subsequent Adverse Neurodevelopmental Outcomes in Mammals |
AOP-Wiki AOPs |
| Aromatase (Cyp19a1) reduction leading to impaired fertility in adult female |
AOP-Wiki AOPs |
| Epigenetic modification of PPARG leading to adipogenesis |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony death/failure 1 |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation contributes to abnormal role change within the worker bee caste leading to colony death failure 1 |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation contributes to impaired hive thermoregulation and leads to colony loss/failure |
AOP-Wiki AOPs |
| Upregulation of Thyroid Hormone Catabolism via Activation of Hepatic Nuclear Receptors, and Subsequent Adverse Neurodevelopmental Outcomes in Mammals |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation contributes to accumulation of damaged mitochondrial DNA and leads to colony loss/failure |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation contributes to abnormal foraging and leads to colony loss/failure via abnormal role change within caste |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation followed by desensitization contributes to abnormal foraging and directly leads to colony loss/failure |
AOP-Wiki AOPs |
| Nicotinic acetylcholine receptor activation contributes to abnormal roll change within the worker bee caste leading to colony loss/failure 2 |
AOP-Wiki AOPs |
| Axonal sodium channel modulation leading to acute mortality |
AOP-Wiki AOPs |
| 5-hydroxytryptamine transporter (5-HTT; SERT) inhibition leading to population decline |
AOP-Wiki AOPs |
| NanoFASE Soil-water-organism model |
NanoSolveIT Tools |
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