Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Use of reduced graphs to encode bioisosterism for similarity-based virtual screening

This paper describes a project to include explicit information about bioisosteric equivalences between pairs of fragment substructures in a system for similarity-based virtual screening. Data from the BIOSTER database show that reduced graphs provide a simple way of encoding known bioisosteric equivalences in a manner that can be used during similarity searching. Scaffold-hopping experiments with the WOMBAT database show that including such information enables similarities to be identified between the reference structures and active structures from the database that contain different, but equivalent, fragment substructures. However, such equivalences also contribute to the similarities between the reference structures and inactives, and the latter equivalences can swamp those involving the actives. This presents serious problems for the routine use of information about bioisosteric fragments in similarity-based virtual screening

QUASI: a novel method for simultaneous superposition of multiple flexible ligands and virtual screening using partial similarity

The structure of many receptors is unknown, and only information about diverse ligands binding to them is available. A new method is presented for the superposition of such ligands, derivation of putative receptor site models and utilization of the models for screening of compound databases. In order to generate a receptor model, the similarity of all ligands is optimized simultaneously taking into account conformational flexibility and also the possibility that the ligands can bind to different regions of the site and only partially overlap. Ligand similarity is defined with respect to a receptor site model serving as a common reference frame. The receptor model is dynamic and coevolves with the ligand alignment until an optimal self-consistent superposition is achieved. When ligand conformational flexibility is permitted, different superposition models are possible and consistent with the data. Clustering of the superposition solutions is used to obtain diverse models. When the models are used to screen a database of compounds, high enrichments are obtained, comparable to those obtained in docking studies. © 2007 American Chemical Society