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

Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

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

Analysis of Random Fragment Profiles for the Detection of Structure-Activity Relationships

Substructure- or fragment-type descriptors are effective and widely used tools for chemical similarity searching and other applications in chemoinformatics and computer-aided drug discovery. Therefore, a large number of well-defined computational fragmentation schemes has been devised including hierarchical fragmentation of molecules for the analysis of core structures in drugs or retrosynthetic fragmentation of compounds for de novo ligand design. Furthermore, the generation of dictionaries of structural key-type descriptors that are important tools in pharmaceutical research involves knowledge-based fragment design. Currently more than 5 000 standard descriptors are available for the representation of molecular structures, and therefore the selection of suitable combinations of descriptors for specific chemoinformatic applications is a crucial task. This thesis departs from well-defined substructure design approaches. Randomly generated fragment populations are generated and mined for substructures associated with different compound classes. A novel method termed MolBlaster is introduced for the evaluation of molecular similarity relationships on the basis of randomly generated fragment populations. Fragment profiles of molecules are generated by random deletion of bonds in connectivity tables and quantitatively compared using entropy-based metrics. In test calculations, MolBlaster accurately reproduced a structural key-based similarity ranking of druglike molecules. To adapt the generation and comparison of random fragment populations for largescale compound screening, different fragmentation schemes are compared and a novel entropic similarity metric termed PSE is introduced for compound ranking. The approach is extensively tested on different compound activity classes with varying degrees of intra-class structural diversity and produces promising results in these calculations, comparable to similarity searching using state-of-the-art fingerprints. These results demonstrate the potential of randomly generated fragments for the detection of structure-activity relationships. Furthermore, a methodology to analyze random fragment populations at the molecular level of detail is introduced. It determines conditional probability relationships between fragments. Random fragment profiles are generated for an arbitrary set of molecules, and a frequency vector is assigned for each observed fragment. An algorithm is designed to compare frequency vectors and derive dependencies of fragment occurrence. Using calculated dependency values, random fragment populations can be organized in graphs that capture their relationships and make it possible to map fragment pathways of biologically active molecules. For sets of molecules having similar activity, unique fragment signatures, so-called Activity Class Characteristic Substructures (ACCS), are identified. Random fragment profiles are found to contain compound class-specific information and activity-specific fragment hierarchies. In virtual screening trials, short ACCS fingerprints perform well on many compound classes when compared to more complex state-of-the-art 2D fingerprints. In order to elucidate potential reasons for the high predictive utility of ACCS a thorough systematic analysis of their distribution in active and database compounds have been carried out. This reveals that the discriminatory power of ACCS results from the rare occurrence of individual and combinations of ACCS in screening databases. Furthermore, it is shown that ACCS sets isolated from random populations are typically found to form coherent molecular cores in active compounds. Characteristic core regions are already formed by small numbers of substructures and remain stable when more fragments are added. Thus, classspecific random fragment hierarchies encode meaningful structural information, providing a structural rationale for the signature character of activity-specific fragment hierarchies. It follows that compound-class-directed structural descriptors that do not depend on the application of predefined fragmentation or design schemes can be isolated from random fragment populations

Computational Methods for the Integration of Biological Activity and Chemical Space

One general aim of medicinal chemistry is the understanding of structure-activity relationships of ligands that bind to biological targets. Advances in combinatorial chemistry and biological screening technologies allow the analysis of ligand-target relationships on a large-scale. However, in order to extract useful information from biological activity data, computational methods are needed that link activity of ligands to their chemical structure. In this thesis, it is investigated how fragment-type descriptors of molecular structure can be used in order to create a link between activity and chemical ligand space. First, an activity class-dependent hierarchical fragmentation scheme is introduced that generates fragmentation pathways that are aligned using established methodologies for multiple alignment of biological sequences. These alignments are then used to extract consensus fragment sequences that serve as a structural signature for individual biological activity classes. It is also investigated how defined, chemically intuitive molecular fragments can be organized based on their topological environment and co-occurrence in compounds active against closely related targets. Therefore, the Topological Fragment Index is introduced that quantifies the topological environment complexity of a fragment in a given molecule, and thus goes beyond fragment frequency analysis. Fragment dependencies have been established on the basis of common topological environments, which facilitates the identification of activity class-characteristic fragment dependency pathways that describe fragment relationships beyond structural resemblance. Because fragments are often dependent on each other in an activity class-specific manner, the importance of defined fragment combinations for similarity searching is further assessed. Therefore, Feature Co-occurrence Networks are introduced that allow the identification of feature cliques characteristic of individual activity classes. Three differently designed molecular fingerprints are compared for their ability to provide such cliques and a clique-based similarity searching strategy is established. For molecule- and activity class-centric fingerprint designs, feature combinations are shown to improve similarity search performance in comparison to standard methods. Moreover, it is demonstrated that individual features can form activity-class specific combinations. Extending the analysis of feature cliques characteristic of individual activity classes, the distribution of defined fragment combinations among several compound classes acting against closely related targets is assessed. Fragment Formal Concept Analysis is introduced for flexible mining of complex structure-activity relationships. It allows the interactive assembly of fragment queries that yield fragment combinations characteristic of defined activity and potency profiles. It is shown that pairs and triplets, rather than individual fragments distinguish between different activity profiles. A classifier is built based on these fragment signatures that distinguishes between ligands of closely related targets. Going beyond activity profiles, compound selectivity is also analyzed. Therefore, Molecular Formal Concept Analysis is introduced for the systematic mining of compound selectivity profiles on a whole-molecule basis. Using this approach, structurally diverse compounds are identified that share a selectivity profile with selected template compounds. Structure-selectivity relationships of obtained compound sets are further analyzed

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models