Filtering and counting of extended connectivity fingerprint features maximizes compound recall and the structural diversity of hits.
Extended connectivity fingerprints produce variable numbers of structural features for molecules and quantitative comparison of feature ensembles is typically carried out as a measure of molecular similarity. As an alternative way to utilize the information content of extended connectivity fingerprint features, we have introduced a compound class-directed feature filtering technique. In combination with a simple feature counting protocol, feature filtering significantly improves the performance of extended connectivity fingerprint similarity searching compared with state-of-the-art fingerprint search methods. Subsets of extended connectivity fingerprint features that are unique to active compounds are found to be responsible for high compound recall. Moreover, feature filtering and counting is shown to result in significantly higher scaffold hopping potential than data fusion or fingerprint averaging methods. Extended connectivity fingerprint feature filtering and counting represents one of the simplest similarity search methods introduced to date, yet it produces top compound recall and maximizes the scaffold diversity of hits, which is a longstanding goal of similarity searching.
Hu
Ye
Y
Lounkine
Eugen
E
Bajorath
Jürgen
J
D003205:Computing Methodologies; D016208:Databases, Factual; D055808:Drug Discovery; D015233:Models, Statistical; D009930:Organic Chemicals / Q000737:chemistry; D011506:Proteins / Q000037:antagonists & inhibitors / Q000737:chemistry / Q000378:metabolism
D009930:Organic Chemicals; D011506:Proteins