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binary classification model  (TargetMol)


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    Structured Review

    TargetMol binary classification model
    Binary Classification Model, supplied by TargetMol, used in various techniques. Bioz Stars score: 94/100, based on 26 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/binary+classification+model/Bioactive+Compounds+Library+Max/pmc12828150-63-11-32
    Average 94 stars, based on 26 article reviews
    binary classification model - by Bioz Stars, 2026-10
    94/100 stars

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    Related Articles

    Activity Assay:

    Article Title: Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy
    Article Snippet: Data are shown as mean ± SD ( n = 3).Data are shown as mean ± SD ( n = 3).. Figure 2 To validating the genuine accuracy of the model, the binary classification model was applied to screen the antitumor agents from L4010 Database (a commercial compound dataset containing 20,952 compounds from TargetMol) ( A and B).. Firstly, in order to meet the requirement that the selected molecule can be further optimized as a hit as a structural fragment with antitumor potential, we first selected molecules with molecular weights ranging from 200 to 500, and obtained 16,516 molecules.Firstly, in order to meet the requirement that the selected molecule can be further optimized as a hit as a structural fragment with antitumor potential, we first selected molecules with molecular weights ranging from 200 to 500, and obtained 16,516 molecules.

    Article Title: Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy
    Article Snippet: The ROC-AUC scores of RF and SVM model on the test set were 0.901 and 0.874, respectively, both of which performed noticeably worse than the XGBoost method (Supporting Information Figs. S2 and S3).The ROC-AUC scores of RF and SVM model on the test set were 0.901 and 0.874, respectively, both of which performed noticeably worse than the XGBoost method (Supporting Information Figs. S2 and S3).. To validating the genuine accuracy of the model, the binary classification model was applied to screen the antitumor agents from L4010 Database (a commercial compound dataset containing 20,952 compounds from TargetMol) (Fig. 2A and B).. Firstly, in order to meet the requirement that the selected molecule can be further optimized as a hit as a structural fragment with antitumor potential, we first selected molecules with molecular weights ranging from 200 to 500, and obtained 16,516 molecules.Firstly, in order to meet the requirement that the selected molecule can be further optimized as a hit as a structural fragment with antitumor potential, we first selected molecules with molecular weights ranging from 200 to 500, and obtained 16,516 molecules.

    Drug discovery:

    Article Title: Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy
    Article Snippet: Data are shown as mean ± SD ( n = 3).Data are shown as mean ± SD ( n = 3).. Figure 2 To validating the genuine accuracy of the model, the binary classification model was applied to screen the antitumor agents from L4010 Database (a commercial compound dataset containing 20,952 compounds from TargetMol) ( A and B).. Firstly, in order to meet the requirement that the selected molecule can be further optimized as a hit as a structural fragment with antitumor potential, we first selected molecules with molecular weights ranging from 200 to 500, and obtained 16,516 molecules.Firstly, in order to meet the requirement that the selected molecule can be further optimized as a hit as a structural fragment with antitumor potential, we first selected molecules with molecular weights ranging from 200 to 500, and obtained 16,516 molecules.

    Article Title: Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy
    Article Snippet: The ROC-AUC scores of RF and SVM model on the test set were 0.901 and 0.874, respectively, both of which performed noticeably worse than the XGBoost method (Supporting Information Figs. S2 and S3).The ROC-AUC scores of RF and SVM model on the test set were 0.901 and 0.874, respectively, both of which performed noticeably worse than the XGBoost method (Supporting Information Figs. S2 and S3).. To validating the genuine accuracy of the model, the binary classification model was applied to screen the antitumor agents from L4010 Database (a commercial compound dataset containing 20,952 compounds from TargetMol) (Fig. 2A and B).. Firstly, in order to meet the requirement that the selected molecule can be further optimized as a hit as a structural fragment with antitumor potential, we first selected molecules with molecular weights ranging from 200 to 500, and obtained 16,516 molecules.Firstly, in order to meet the requirement that the selected molecule can be further optimized as a hit as a structural fragment with antitumor potential, we first selected molecules with molecular weights ranging from 200 to 500, and obtained 16,516 molecules.



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