Knowledge Base for Nuclear Receptor Drug Discovery

Antolín, Albert A.; Mestres, Jordi

doi:10.1002/9781118185537.ch8

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…From our own theoretical studies, approximately 11 macromolecular targets on average are expected for a given druglike compound . Similar investigations have reported 3–10 targets per drug, depending on the target class and potency levels considered …”

Section: Playing Dirtysupporting

confidence: 64%

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De Novo Design at the Edge of Chaos

2016

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Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.

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Section: Playing Dirtysupporting

confidence: 64%

“…73 Similar investigations have reported 3− 10 targets per drug, depending on the target class and potency levels considered. 74 The trouble frequently begins when the assay results arrive and the most promising reported active compounds ("hits") must be selected. Which of them are "true positives" and merit follow-up?…”

Section: ■ Playing Dirtymentioning

confidence: 99%

De Novo Design at the Edge of Chaos

2016

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Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction

Button

Hiss

Schneider³

2016

Molecular Informatics

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Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template-derived designs using ligand-based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction-based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions. Keeping in mind the probabilistic nature of the methods, such a combination of fast and meaningful computational structure generation by reaction-based design and product scoring by target class prediction may be appropriate for prospective application in medicinal chemistry.

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Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Reker

Rodrigues

Schneider

2014

Proc. Natl. Acad. Sci. U.S.A.

205

165

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De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map-based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibraterelated compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.drug design | target prediction | polypharmacology | machine learning | chemical similarity C omputer-assisted de novo molecular design has evolved as a popular source of ideas to combat the perceived lack of new chemical entities (NCEs) in chemical biology and drug discovery (1). We demonstrate that automated de novo design delivers readily synthesizable NCEs with desirable activity profiles. Although receptor-based design operates on a model of a macromolecular binding site, ligand-based methods are either explicitly or implicitly based on the chemical similarity principle (2) without requiring a receptor model (3). Instead, the latter typically uses some measure of chemical or pharmacophore feature similarity to a reference ligand as a fitness function, which aims to generate NCEs as template mimetics via scaffold hopping (4-8). We report the development, implementation, and successful prospective application of an innovative computational technique for the target profiling of de novo-designed molecules. The approach combines the concepts of self-organizing maps (SOMs) (9) for macromolecular target prediction (10), consensus scoring (11), and a statistical evaluation that provides confidence estimates for the predictions.Predicting polypharmacological activities is a topic relevant to chemical biology and drug discovery, not only to take advantage of inherent drug promiscuity but also to decrease lead compound attrition caused by unfavorable off-target modulation

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Knowledge Base for Nuclear Receptor Drug Discovery

Cited by 3 publications

References 37 publications

De Novo Design at the Edge of Chaos

De Novo Design at the Edge of Chaos

Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

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