Skip to main content


Fig. 4 | Journal of Cheminformatics

Fig. 4

From: Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes

Fig. 4

Average clustering coefficient of similarity networks in the function of the similarity threshold. For all datasets it is possible to identify a peak that stands out in comparison with the others by spanning the largest range of similarity threshold t. The threshold associated with the highest ACC value in the peak is denoted as t α , i.e. the so-called obvious local maximum of the ACC(t) function. Fingerprint: ECFP_4, similarity measure: Tanimoto similarity-coefficient. a SCL dataset. b WOMBAT dataset. c PubChem MLSMR dataset

Back to article page