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

The Joint European Compound Library:boosting precompetitive research

The Joint European Compound Library (JECL) is a new high-throughput screening collection aimed at driving precompetitive drug discovery and target validation. The JECL has been established with a core of over 321000 compounds from the proprietary collections of seven pharmaceutical companies and will expand to around 500000 compounds. Here, we analyse the physicochemical profile and chemical diversity of the core collection, showing that the collection is diverse and has a broad spectrum of predicted biological activity. We also describe a model for sharing compound information from multiple proprietary collections, enabling diversity and quality analysis without disclosing structures. The JECL is available for screening at no cost to European academic laboratories and SMEs through the IMI European Lead Factory (http://www.europeanleadfactory.eu/)

New compound sets identified from high throughput phenotypic screening against three kinetoplastid parasites:an open resource

Using whole-cell phenotypic assays, the GlaxoSmithKline high-throughput screening (HTS) diversity set of 1.8 million compounds was screened against the three kinetoplastids most relevant to human disease, i.e. Leishmania donovani, Trypanosoma cruzi and Trypanosoma brucei. Secondary confirmatory and orthogonal intracellular anti-parasiticidal assays were conducted, and the potential for non-specific cytotoxicity determined. Hit compounds were chemically clustered and triaged for desirable physicochemical properties. The hypothetical biological target space covered by these diversity sets was investigated through bioinformatics methodologies. Consequently, three anti-kinetoplastid chemical boxes of ~200 compounds each were assembled. Functional analyses of these compounds suggest a wide array of potential modes of action against kinetoplastid kinases, proteases and cytochromes as well as potential host–pathogen targets. This is the first published parallel high throughput screening of a pharma compound collection against kinetoplastids. The compound sets are provided as an open resource for future lead discovery programs, and to address important research questions.The support and funding of Tres Cantos Open Lab Foundation is gratefully acknowledgedPeer reviewe

Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

Evolutionary conservation of influenza A PB2 sequences reveals potential target sites for small molecule inhibitors.

The influenza A basic polymerase protein 2 (PB2) functions as part of a heterotrimer to replicate the viral RNA genome. To investigate novel PB2 antiviral target sites, this work identified evolutionary conserved regions across the PB2 protein sequence amongst all sub-types and hosts, as well as ligand binding hot spots which overlap with highly conserved areas. Fifteen binding sites were predicted in different PB2 domains; some of which reside in areas of unknown function. Virtual screening of ~50,000 drug-like compounds showed binding affinities of up to 10.3 kcal/mol. The highest affinity molecules were found to interact with conserved residues including Gln138, Gly222, Ile529, Asn540 and Thr530. A library containing 1738 FDA approved drugs were screened additionally and revealed Paliperidone as a top hit with a binding affinity of -10 kcal/mol. Predicted ligands are ideal leads for new antivirals as they were targeted to evolutionary conserved binding sites

A Small Molecule Scaffold Analysis: Biological Profiles of Novel, Privileged, and Promiscuous Scaffolds

Owing to the better understanding of physiochemical and biological challenges associated with the bioactivity of small organic molecules, novel scaffolds are becoming more extensively engineered and more highly optimized. Currently, binding specificity is becoming intensively investigated and a priority to many drug designers because it reflects the quality of scaffolds. In this thesis, we introduce a comparative analysis to benchmark the quality of four novel scaffold libraries prepared by the University of Pittsburgh Centers for Chemical Methodologies and Library Development (UPCMLD). The bioactivity profiles of these novel scaffold libraries, namely UPCMLD09A, UPCMLD02A, UPCMLD16A, and UPCMLD24A, were explored and compared with those of two privileged (benzodiazepines and dihydropyridines) and two promiscuous (rhodanines and quinolines) scaffold libraries from the literature. The approach we implemented is intended to provide an unbiased analysis and comparison of novel, privileged and promiscuous scaffold libraries. The results for three of the UPCMLD libraries, UPCMLD09A, UPCMLD16A and UPCMLD24A, indicate a comparatively high selectivity. Therefore, we suggest that the scaffolds of these libraries could be considered as privileged. In contrast, a promiscuous profile for some compounds was observed in one of the UPCMLD libraries, UPCMLD02A

A Fragment-Based Approach for the Development of G-Quadruplex Ligands: Role of the Amidoxime Moiety

G-quadruplex (G4) nucleic acid structures have been reported to be involved in several human pathologies, including cancer, neurodegenerative disorders and infectious diseases; however, G4 targeting compounds still need implementation in terms of drug-like properties and selectivity in order to reach the clinical use. So far, G4 ligands have been mainly identified through high-throughput screening methods or design of molecules with pre-set features. Here, we describe the development of new heterocyclic ligands through a fragment-based drug discovery (FBDD) approach. The ligands were designed against the major G4 present in the long terminal repeat (LTR) promoter region of the human immunodeficiency virus-1 (HIV-1), the stabilization of which has been shown to suppress viral gene expression and replication. Our method is based on the generation of molecular fragment small libraries, screened against the target to further elaborate them into lead compounds. We screened 150 small molecules, composed by structurally and chemically different fragments, selected from commercially available and in-house compounds; synthetic elaboration yielded several G4 ligands and two final G4 binders, both embedding an amidoxime moiety; one of these two compounds showed preferential binding for the HIV-1 LTR G4. This work presents the discovery of a novel potential pharmacophore and highlights the possibility to apply a fragment-based approach to develop G4 ligands with unexpected chemical features

Rational methods for the selection of diverse screening compounds.

Traditionally a pursuit of large pharmaceutical companies, high-throughput screening assays are becoming increasingly common within academic and government laboratories. This shift has been instrumental in enabling projects that have not been commercially viable, such as chemical probe discovery and screening against high-risk targets. Once an assay has been prepared and validated, it must be fed with screening compounds. Crafting a successful collection of small molecules for screening poses a significant challenge. An optimized collection will minimize false positives while maximizing hit rates of compounds that are amenable to lead generation and optimization. Without due consideration of the relevant protein targets and the downstream screening assays, compound filtering and selection can fail to explore the great extent of chemical diversity and eschew valuable novelty. Herein, we discuss the different factors to be considered and methods that may be employed when assembling a structurally diverse compound collection for screening. Rational methods for selecting diverse chemical libraries are essential for their effective use in high-throughput screens.We are grateful for financial support from the MRC, Wellcome Trust, CRUK, EPSRC, BBSRC and Newman Trust.This is the author accepted manuscript. The final version is available from American Chemical Society via http://dx.doi.org/10.1021/cb100420