Ligand-based virtual screening using binary kernel discrimination

This paper discusses the use of a machine-learning technique called binary kernel discrimination (BKD) for virtual screening in drug- and pesticide-discovery programmes. BKD is compared with several other ligand-based tools for virtual screening in databases of 2D structures represented by fragment bit-strings, and is shown to provide an effective, and reasonably efficient, way of prioritising compounds for biological screening

Rapid Quantification of Molecular Diversity for Selective Database Acquisition

There is an increasing need to expand the structural diversity of the molecules investigated in lead-discovery programs. One way in which this can be achieved is by acquiring external datasets that will enhance an existing database. This paper describes a rapid procedure for the selection of external datasets using a measure of structural diversity that is calculated from sums of pairwise intermolecular structural similarities

Similarity Methods in Chemoinformatics

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Similarity-based virtual screening using bayesian inference network: enhanced search using 2D fingerprints and multiple reference structures

It has been known that different reference structure retrieve different sets of structures. Recent works in similarity searching have suggested that significant improvements in retrieval effectiveness can be achieved by combining results from different reference structures. One of an important characteristic of the Bayesian inference network (BIN) model is that permits the combining of multiple reference structures. In this paper we introduce a formal inference net model to directly combine the contributions of multiple reference structures, and propose a novel approach to the combination of information from various reference structures. The inference net model of similarity, which was designed from this point of view, treats similarity searching as an evidential reasoning process where multiple sources of evidence about target structure are combined to estimate similarity scores. In this paper, we have compared BIN with other similarity searching methods when multiple bioactive reference structures are available. Six different 2D fingerprints were used in combination with data fusion (DF) and nearest neighbor (NN) approaches as search tools and also as descriptors for BIN. Our empirical results show that the BIN consistently outperformed all conventional approaches such as DF and NN, regardless of the fingerprints that were tested. The superiority of BIN over conventional approaches is ascribed to the fact that BIN understands the content of the descriptors of the structures and references and used this understanding to infer the direct relationship between structures and references