Browsing by Model type "Neural network ensemble (classification)"

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Browsing by Model type "Neural network ensemble (classification)"

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  • Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_102+wp2). 2020. 
    Published: Michael Lawless (2021-09-02)
    Abstract: MUT_102+wp2 The Ames bacterial mutagenicity test is an important regulatory screen for potential carcinogenicity. In silico prediction of Ames positivity plays two important but somewhat distinct roles in the discovery and development of biologically active compounds. A positive Ames test does not necessarily derail development of an active ingredient (AI), ...
    HumanHealth Classification QMRF
  • Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_1535). 2020. 
    Published: Michael Lawless (2021-09-02)
    Abstract: MUT_1535 The Ames bacterial mutagenicity test is an important regulatory screen for potential carcinogenicity. In silico prediction of Ames positivity plays two important but somewhat distinct roles in the discovery and development of biologically active compounds. A positive Ames test does not necessarily derail development of an active ingredient (AI), ...
    HumanHealth Classification QMRF
  • Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_m97+1537). 2020. 
    Published: Michael Lawless (2021-09-02)
    Abstract: MUT_m97+1537 The Ames bacterial mutagenicity test is an important regulatory screen for potential carcinogenicity. In silico prediction of Ames positivity plays two important but somewhat distinct roles in the discovery and development of biologically active compounds. A positive Ames test does not necessarily derail development of an active ingredient (AI), ...
    HumanHealth Classification QMRF