New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching.
Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching.
Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, U.K.
Jérôme
J
Hert
Peter
P
Willett
David J
DJ
Wilton
Pierre
P
Acklin
Kamal
K
Azzaoui
Edgar
E
Jacoby
Ansgar
A
Schuffenhauer
D001185:Artificial Intelligence; D019295:Computational Biology / Q000639:trends*; D003198:Computer Simulation; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008024:Ligands; D016571:Neural Networks, Computer; D013329:Structure-Activity Relationship
D008024:Ligands