Biomedical subjects

Peter Willett

Publications and source records attributed to Peter Willett.

At least 37 records · Page 2Linked to original sources

PubMed2002

Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases.

This paper reports an evaluation of both graph-based and fingerprint-based measures of structural similarity, when used for virtual screening of sets of 2D molecules drawn from the MDDR and ID Alert databases. The graph-based measures employ a new maximum common edge subgraph isomorphism algorithm, called RASCAL, with several similarity coefficients described previously for quantifying the similarity between pairs of graphs. The effectiveness of these graph-based searches is compared with that resulting from similarity searches using BCI, Daylight and Unity 2D fingerprints. Our results suggest that graph-based approaches provide an effective complement to existing fingerprint-based approaches to virtual screening.

Algorithms↗

PubMed2002

Maximum common subgraph isomorphism algorithms for the matching of chemical structures.

The maximum common subgraph (MCS) problem has become increasingly important in those aspects of chemoinformatics that involve the matching of 2D or 3D chemical structures. This paper provides a classification and a review of the many MCS algorithms, both exact and approximate, that have been described in the literature, and makes recommendations regarding their applicability to typical chemoinformatics tasks.

Algorithms↗

PubMedDate not supplied

Combinatorial library design using a multiobjective genetic algorithm.

Early results from screening combinatorial libraries have been disappointing with libraries either failing to deliver the improved hit rates that were expected or resulting in hits with characteristics that make them undesirable as lead compounds. Consequently, the focus in library design has shifted toward designing libraries that are optimized on multiple properties simultaneously, for example, diversity and "druglike" physicochemical properties. Here we describe the program MoSELECT that is based on a multiobjective genetic algorithm and which is able to suggest a family of solutions to multiobjective library design where all the solutions are equally valid and each represents a different compromise between the objectives. MoSELECT also allows the relationships between the different objectives to be explored with competing objectives easily identified. The library designer can then make an informed choice on which solution(s) to explore. Various performance characteristics of MoSELECT are reported based on a number of different combinatorial libraries.

Algorithms↗

PubMedDate not supplied

Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm.

Recently a method (RASCAL) for determining graph similarity using a maximum common edge subgraph algorithm has been proposed which has proven to be very efficient when used to calculate the relative similarity of chemical structures represented as graphs. This paper describes heuristics which simplify a RASCAL similarity calculation by taking advantage of certain properties specific to chemical graph representations of molecular structure. These heuristics are shown experimentally to increase the efficiency of the algorithm, especially at more distant values of chemical graph similarity.

Journal Article↗

PubMedDate not supplied

Generation and display of activity-weighted chemical hyperstructures.

A chemical hyperstructure is a single graph representation of a set of molecules that minimizes the degree of structural redundancy in the data set. This paper describes the use of a genetic algorithm to generate an activity-weighted chemical hyperstructure (AWCH) by sequentially mapping each molecule in the data set to the hyperstructure and then assigning activity and inactivity frequency weights to the nodes and edges of the hyperstructure. Experiments with several data sets demonstrate the level of activity clustering in an AWCH.

Journal Article↗

PubMedDate not supplied

Comparison of ranking methods for virtual screening in lead-discovery programs.

This paper discusses the use of several rank-based virtual screening methods for prioritizing compounds in lead-discovery programs, given a training set for which both structural and bioactivity data are available. Structures from the NCI AIDS data set and from the Syngenta corporate database were represented by two types of fragment bit-string and by sets of high-level molecular features. These representations were processed using binary kernel discrimination, similarity searching, substructural analysis, support vector machine, and trend vector analysis, with the effectiveness of the methods being judged by the extent to which active test set molecules were clustered toward the top of the resultant rankings. The binary kernel discrimination approach yielded consistently superior rankings and would appear to have considerable potential for chemical screening applications.

Journal Article↗

PubMedDate not supplied

Calculation of intersubstituent similarity using R-group descriptors.

This paper discusses the calculation of the similarities between pairs of substituents on ring systems. An R-group descriptor characterizes the distribution of some atom-based property, such as elemental type or partial atomic charge, at increasing numbers of bonds distant from the point of substitution on the parent ring. The similarity between a pair of descriptors is then calculated by a comparison of the corresponding property vectors. Experiments with the BIOSTER database demonstrate the ability of such similarity measures to discriminate between bioisosteric and nonbioisosteric functional groups.

Journal Article↗

PubMedDate not supplied

Evaluation of similarity measures for searching the dictionary of natural products database.

Similarity searches using combinations of seven different similarity coefficients and six different representations have been carried out on the Dictionary of Natural Products database. The objective was to discover if any special methods of searching apply to this database, which is very different in nature from the many synthetic databases that have been the subject of previous studies of similarity searching. Search effectiveness was assessed by a recall analysis of the search outputs from sets of pharmacologically active target structures. The different target sets produce exceptional but contradictory results for the Russell-Rao and Forbes coefficients, which have been shown to be due to a dependence on molecular size; these are the coefficients of choice in the case of large and small structures, respectively. Rankings from these results have been combined using a data fusion scheme and some small gains in performance were normally obtained by using substructural fingerprints and molecular holograms in combination with the Squared Euclidean or Tanimoto coefficients.

Biological Products↗

PubMedDate not supplied

Similarity searching using reduced graphs.

Reduced graphs provide summary representations of chemical structures. In this work, the effectiveness of reduced graphs for similarity searching is investigated. Different types of reduced graphs are introduced that aim to summarize features of structures that have the potential to form interactions with receptors while retaining the topology between the features. Similarity searches have been carried out across a variety of different activity classes. The effectiveness of the reduced graphs at retrieving compounds with the same activity as known target compounds is compared with searching using Daylight fingerprints. The reduced graphs are shown to be effective for similarity searching and to retrieve more diverse active compounds than those found using Daylight fingerprints; they thus represent a complementary similarity searching tool.

Journal Article↗

PubMedDate not supplied

Combination of fingerprint-based similarity coefficients using data fusion.

Many different types of similarity coefficients have been described in the literature. Since different coefficients take into account different characteristics when assessing the degree of similarity between molecules, it is reasonable to combine them to further optimize the measures of similarity between molecules. This paper describes experiments in which data fusion is used to combine several binary similarity coefficients to get an overall estimate of similarity for searching databases of bioactive molecules. The results show that search performances can be improved by combining coefficients with little extra computational cost. However, there is no single combination which gives a consistently high performance for all search types.

Journal Article↗

PubMedDate not supplied

Searching for patterns of amino acids in 3D protein structures.

This paper describes the program ASSAM, which has been developed to search for patterns of amino acid side-chains in the 3D structures in the Protein Data Bank. ASSAM represents an amino acid by a vector drawn from the main chain towards the functional part of the amino acid and then computes a graph representation of a protein in which the individual side-chain vectors are the nodes and the intervector distances are the edges. The presence of a query pattern in a Protein Data Bank structure can then be searched for by means of a subgraph isomorphism algorithm. Recent enhancements to ASSAM allow searches to include the following: the main-chain structure in addition to the side-chains; the secondary structure and solvent accessibility of side-chains; allowable distances from a known binding-site; disulfide bridges; and improved generic and wild-card queries. The effectiveness of these approaches is demonstrated by extensive searches of the Protein Data Bank for typical 3D query patterns.

Algorithms↗

PubMedDate not supplied

CLIP: similarity searching of 3D databases using clique detection.

This paper describes a program for 3D similarity searching, called CLIP (for Candidate Ligand Identification Program), that uses the Bron-Kerbosch clique detection algorithm to find those structures in a file that have large structures in common with a target structure. Structures are characterized by the geometric arrangement of pharmacophore points and the similarity between two structures calculated using modifications of the Simpson and Tanimoto association coefficients. This modification takes into account the fact that a distance tolerance is required to ensure that pairs of interatomic distances can be regarded as equivalent during the clique-construction stage of the matching algorithm. Experiments with HIV assay data demonstrate the effectiveness and the efficiency of this approach to virtual screening.

Journal Article↗

PubMedDate not supplied

Analysis and display of the size dependence of chemical similarity coefficients.

We discuss the size-bias inherent in several chemical similarity coefficients when used for the similarity searching or diversity selection of compound collections. Limits to the upper bounds of 14 standard similarity coefficients are investigated, and the results are used to identify some exceptional characteristics of a few of the coefficients. An additional numerical contribution to the known size bias in the Tanimoto coefficient is identified. Graphical plots with respect to relative bit density are introduced to further assess the coefficients. Our methods reveal the asymmetries inherent in most similarity coefficients that lead to bias in selection, most notably with the Forbes and Russell-Rao coefficients. Conversely, when applied to the recently introduced Modified Tanimoto coefficient our methods provide support for the view that it is less biased toward molecular size than most. In this work we focus our discussion on fragment-based bit strings, but we demonstrate how our approach can be generalized to continuous representations.

Journal Article↗

PubMedDate not supplied

Similarity searching in databases of flexible 3D structures using smoothed bounded distance matrices.

This paper describes a method for calculating the similarity between pairs of chemical structures represented by 3D molecular graphs. The method is based on a graph matching procedure that accommodates conformational flexibility by using distance ranges between pairs of atoms, rather than fixing the atom pair distances. These distance ranges are generated using triangle and tetrangle bound smoothing techniques from distance geometry. The effectiveness of the proposed method in retrieving other compounds of like biological activity is evaluated, and the results are compared with those obtained from other, 2D-based methods for similarity searching.

Journal Article↗

PubMedDate not supplied

Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures.

Fingerprint-based similarity searching is widely used for virtual screening when only a single bioactive reference structure is available. This paper reviews three distinct ways of carrying out such searches when multiple bioactive reference structures are available: merging the individual fingerprints into a single combined fingerprint; applying data fusion to the similarity rankings resulting from individual similarity searches; and approximations to substructural analysis. Extended searches on the MDL Drug Data Report database suggest that fusing similarity scores is the most effective general approach, with the best individual results coming from the binary kernel discrimination technique.

Molecular Structure↗

PubMedDate not supplied

Clustering files of chemical structures using the fuzzy k-means clustering method.

This paper evaluates the use of the fuzzy k-means clustering method for the clustering of files of 2D chemical structures. Simulated property prediction experiments with the Starlist file of logP values demonstrate that use of the fuzzy k-means method can, in some cases, yield results that are superior to those obtained with the conventional k-means method and with Ward's clustering method. Clustering of several small sets of agrochemical compounds demonstrate the ability of the fuzzy k-means method to highlight multicluster membership and to identify outlier compounds, although the former can be difficult to interpret in some cases.

Journal Article↗

PubMedDate not supplied

Analysis of data fusion methods in virtual screening: similarity and group fusion.

In a recent companion paper we have related the operation of simple data fusion rules used in virtual screening to a multiple integral formalism. In this paper we extend these ideas to the analysis of data fusion methods applied to real data. We examine several cases of similarity fusion using different coefficients and different representations and consider the reasons for positive or negative results in terms of the similarity distributions. Results are obtained using the SUM-, MAX- MIN-, and CombMNZ-fusion rules. We also develop a customized fusion rule, which provides an estimate of the optimal possible result for fusing multiple searches of a specific database; this shows that similarity fusion can, in principle, achieve retrieval enhancements even if this is not achieved in practice with current fusion rules. The methods are extended to analyze the comparatively successful results of group fusion with multiple actives, and we provide a rationale for the observed superiority of the MAX-rule over the SUM-rule in this context.

Combinatorial Chemistry Techniques↗

PubMedDate not supplied

Analysis of data fusion methods in virtual screening: theoretical model.

This paper presents a theoretical model of how data fusion can be used to combine the results of multiple similarity searches of chemical databases. The model is based on frequency distributions of similarity values that are fused using a multiple integration over regions defined by the particular fusion rule that is being applied. For pairwise fusion, the resulting double integrals are straightforward to evaluate for simple model distributions. Similarity values for recovered-active and recovered-nonactive frequency distributions are independently modeled using a constant background, linearly biased terms, and a first-order correlated term. The model shows that two standard fusion rules can give performance enhancements in some cases but that the results of fusion are dependent on many factors that, taken together, can lead to seemingly inconsistent levels of enhancement.

Algorithms↗