A maximum common substructure-based algorithm for searching and predicting drug-like compounds

Motivation: The prediction of biologically active compounds is of great importance for high-throughput screening (HTS) approaches in drug discovery and chemical genomics. Many computational methods in this area focus on measuring the structural similarities between chemical structures. However, traditional similarity measures are often too rigid or consider only global similarities between structures. The maximum common substructure (MCS) approach provides a more promising and flexible alternative for predicting bioactive compounds

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

Molecular Similarity and Xenobiotic Metabolism

MetaPrint2D, a new software tool implementing a data-mining approach for predicting sites of xenobiotic metabolism has been developed. The algorithm is based on a statistical analysis of the occurrences of atom centred circular fingerprints in both substrates and metabolites. This approach has undergone extensive evaluation and been shown to be of comparable accuracy to current best-in-class tools, but is able to make much faster predictions, for the first time enabling chemists to explore the effects of structural modifications on a compound’s metabolism in a highly responsive and interactive manner.MetaPrint2D is able to assign a confidence score to the predictions it generates, based on the availability of relevant data and the degree to which a compound is modelled by the algorithm.In the course of the evaluation of MetaPrint2D a novel metric for assessing the performance of site of metabolism predictions has been introduced. This overcomes the bias introduced by molecule size and the number of sites of metabolism inherent to the most commonly reported metrics used to evaluate site of metabolism predictions.This data mining approach to site of metabolism prediction has been augmented by a set of reaction type definitions to produce MetaPrint2D-React, enabling prediction of the types of transformations a compound is likely to undergo and the metabolites that are formed. This approach has been evaluated against both historical data and metabolic schemes reported in a number of recently published studies. Results suggest that the ability of this method to predict metabolic transformations is highly dependent on the relevance of the training set data to the query compounds.MetaPrint2D has been released as an open source software library, and both MetaPrint2D and MetaPrint2D-React are available for chemists to use through the Unilever Centre for Molecular Science Informatics website.----Boehringer-Ingelhie

The use of MoStBioDat for rapid screening of molecular diversity

MoStBioDat is a uniform data storage and extraction system with an extensive array of tools for structural similarity measures and pattern matching which is essential to facilitate the drug discovery process. Structure-based database screening has recently become a common and efficient technique in early stages of the drug development, shifting the emphasis from rational drug design into the probability domain of more or less random discovery. The virtual ligand screening (VLS), an approach based on high-throughput flexible docking, samples a virtually infinite molecular diversity of chemical libraries increasing the concentration of molecules with high binding affinity. The rapid process of subsequent examination of a large number of molecules in order to optimize the molecular diversity is an attractive alternative to the traditional methods of lead discovery. This paper presents the application of the MoStBioDat package not only as a data management platform but mainly in substructure searching. In particular, examples of the applications of MoStBioDat are discussed and analyze

Understanding Toll-like Receptor Modulation Through Machine Learning

Toll-like receptors (TLRs) represent one of the most fascinating and currently most widely studied immunologic targets, due to their crucial role in forming the first barrier in immune response. The structurally conserved TLRs consist of ten human subtypes (TLR1-TLR10), with a structurally broad range of natural ligands, including lipids, peptides, and ribonucleic acid (RNA), which challenges the rational design of drug-like TLR ligands. Therefore, despite their enormous therapeutic potential as powerful regulators of inflammatory pathways, only few TLR modulators (e.g., Imiquimod) are currently in clinical use. Since no complete and up-to-date repository for known TLR modulators is currently available, we carefully collected and manually curated data to create a Toll-like receptor database (TollDB), the first database which includes all reported small organic druglike molecules targeting TLRs and detailed pharmacological assay conditions used for their characterization. TollDB is freely accessible via https://tolldb.drug-design.de and provides three different search possibilities including a ligand-centered simple search, an advanced search that can retrieve information on biological assays and a structure search. Currently, TollDB contains 4925 datapoints describing 2155 compounds tested in 36 assay types using 553 different assay conditions. Among all the 2155 compounds, 1278 are not reported as TLR ligands by ChEMBL database. Users can retrieve information about the measured inactives and multi-target TLR ligands from TollDB. After statistical analysis for TollDB, we compared the chemical space covered by compounds in TollDB to that covered by the compounds in DrugBank. Next, we explored the matched molecular pairs (MMPs) and activity cliffs, then used docking to explain the activity cliffs between MMPs. After a thorough analysis of the entire database, we used a selected dataset from TollDB to train machine learning models to distinguish active ligands for different subtypes. These validated models can be used for prioritizing hits from virtual screening for chemical synthesis or for biological testing. The curated database can be directly used in many ways, for example, as a validation dataset for pharmacophore model evaluation, as a virtual screening library for drug repurposing or as reference for pharmacological assay design. TollDB represents a unique and useful resource for various research fields such as medicinal chemistry, immunology, computational biology and promotes the use of artificial intelligence in modern drug design campaigns.Toll-like Rezeptoren (TLRs) sind aufgrund ihrer entscheidenden Rolle bei der Bildung der ersten Barriere der Immunantwort eines der faszinierendsten und derzeit am häufigsten untersuchten immunologischen Ziele. Die strukturell konservierten TLRs weisen zehn menschliche Subtypen (TLR1-TLR10) auf. Sie umfassen ein breites strukturelles Spektrum natürlicher Liganden, einschließlich Lipiden, Peptiden und RNA, was das rationale Design von arzneimittelähnlichen TLR-Liganden herausfordernd macht. Daher werden derzeit trotz ihres enormen therapeutischen Potenzials als starke Regulatoren von Entzündungswegen nur wenige TLR-Modulatoren (z. B. Imiquimod) klinisch eingesetzt. Da derzeit keine vollständiges und aktuelles Respository für bekannte TLR Modulatoren verfügbar ist, haben wir sorgfältig Daten gesammelt und manuell überprüft, um eine Toll-like-Rezeptor-Datenbank TollDB zu erstellen. Diese Datenbank enthält alle uns bekannten kleinen organischen arzneimittelähnlichen Moleküle mit detaillierten pharmakologischen Testbedingungen, die für ihre Charakterisierung verwendet wurden. TollDB ist unter https://tolldb.drug-design.de frei zugänglich und bietet drei verschiedene Suchmöglichkeiten, darunter eine Liganden zentrierte einfache Suche, eine erweiterte Suche, mit der Informationen zu biologischen Assays abgerufen werden können, und eine strukturelle Suche. Derzeit enthält TollDB 4925 Datenpunkte, die 2155 Verbindungen beschreiben, die in 36 in vitro Testtypen unter Verwendung von 553 verschiedenen Testbedingungen getestet wurden. Von allen 2155 Verbindungen sind 1278 nicht in der ChEMBL Datenbank enthalten. Benutzer können bei der TollDB auch Informationen zu den gemessenen inaktiven und Multi-Target-TLR-Liganden erhalten. Nach der statistischen Analyse für TollDB haben wir den von den Verbindungen in TollDB abgedeckten chemischen Raum mit dem von den Verbindungen in DrugBank abgedeckten verglichen. Wir haben die matched molecular pairs und activity cliffs untersucht. Nachdem wir ein umfassendes Verständnis der Daten in der TollDB erlangt haben, haben wir die Daten verwendet, um Modelle für maschinelles Lernen zu trainieren, um aktive Liganden für verschiedene Subtypen zu identifizieren. Diese validierten Modelle können zur Priorisierung von Treffern aus dem virtuellen Screening zur Synthese oder zum Testen verwendet werden. Zusammenfassend kann die Datenbank in vielen Aspekten direkt verwendet werden, beispielsweise als Validierungsdatensatz für die Bewertung eines Pharmakophormodells, als virtuelle Screening-Bibliothek für die Umfunktionierung von Arzneimitteln oder als Referenzsubstanz, für das Design des pharmakologischen Assays. TollDB stellt eine einzigartige und nützliche Ressource für verschiedene Forschungsbereiche wie medizinische Chemie, Immunologie und Computerbiologie dar und fördert den Einsatz künstlicher Intelligenz in modernen Wirkstoffdesign

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.Siirretty Doriast

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

Multi-dimensional computational pipeline for large-scale deep screening of compound effect assessment: an in silico case study on ageing-related compounds

Designing alternative approaches to efficiently screen chemicals on the efficacy landscape is a challenging yet indispensable task in the current compound profiling methods. Particularly, increasing regulatory restrictions underscore the need to develop advanced computational pipelines for efficacy assessment of chemical compounds as alternative means to reduce and/or replace in vivo experiments. Here, we present an innovative computational pipeline for large-scale assessment of chemical compounds by analysing and clustering chemical compounds on the basis of multiple dimensions—structural similarity, binding profiles and their network effects across pathways and molecular interaction maps—to generate testable hypotheses on the pharmacological landscapes as well as identify potential mechanisms of efficacy on phenomenological processes. Further, we elucidate the application of the pipeline on a screen of anti-ageing-related compounds to cluster the candidates based on their structure, docking profile and network effects on fundamental metabolic/molecular pathways associated with the cell vitality, highlighting emergent insights on compounds activities based on the multi-dimensional deep screen pipeline