You are viewing the site in preview mode

Skip to main content

Table 1 Collaborative Filtering hyper-parameter tuning: Tabulated top 5 results from training multiple collaborative filtering models using neighborhood methods and matrix factorization methods

From: Implicit-descriptor ligand-based virtual screening by means of collaborative filtering

Distance N. Threshold AUC BEDROC20 EF1%
Neighborhood-based collaborative filtering
 Pearson 10−2 0.791 0.723 4.225
 Pearson 10−5 0.792 0.723 4.225
 Pearson 10−4 0.791 0.723 4.225
 Jaccard 10−2 0.648 0.647 3.670
 Cosine 10−5 0.644 0.647 3.670
Num. Factors SGD step size AUC BEDROC20 EF1%
Matrix factorization-based collaborative filtering
 50 10−3 0.891 0.929 5.476
 32 10−4 0.860 0.889 5.028
 32 10−2 0.899 0.872 4.994
 25 10−4 0.868 0.867 4.765
 25 10−2 0.892 0.847 4.547
Back to article page

Journal of Cheminformatics

ISSN: 1758-2946

Contact us