title: Scaffold hopping in drug discovery using inductive logic programming.
abstract: In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest. We propose a new method for scaffold hopping using inductive logic programming (ILP). ILP uses the observed spatial relationships between pharmacophore types in pretested active and inactive compounds and learns human-readable rules describing the diverse structures of active compounds. The ILP-based scaffold hopping method is compared to two previous algorithms (chemically advanced template search, CATS, and CATS3D) on 10 data sets with diverse scaffolds. The comparison shows that the ILP-based method is significantly better than random selection while the other two algorithms are not. In addition, the ILP-based method retrieves new active scaffolds which were not found by CATS and CATS3D. The results show that the ILP-based method is at least as good as the other methods in this study. ILP produces human-readable rules, which makes it possible to identify the three-dimensional features that lead to scaffold hopping. A minor variant of a rule learnt by ILP for scaffold hopping was subsequently found to cover an inhibitor identified by an independent study. This provides a successful result in a blind trial of the effectiveness of ILP to generate rules for scaffold hopping. We conclude that ILP provides a valuable new approach for scaffold hopping.
authors.affiliation: Computational Bioinformatics Laboratory, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, United Kingdom.
authors.forename: Kazuhisa
authors.initials: K
authors.lastname: Tsunoyama
authors.forename: Ata
authors.initials: A
authors.lastname: Amini
authors.forename: Michael J E
authors.initials: MJ
authors.lastname: Sternberg
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Muggleton
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D015195:Drug Design