Dataset Information

Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries.

ABSTRACT: Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery, and therapeutic repurposing. Multiple libraries are available, but few data-driven approaches exist to compare them and design new libraries. We describe an approach to scoring and creating libraries based on binding selectivity, target coverage, and induced cellular phenotypes as well as chemical structure, stage of clinical development, and user preference. The approach, available via the online tool http://www.smallmoleculesuite.org, assembles sets of compounds with the lowest possible off-target overlap. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them and led us to design a new LSP-OptimalKinase library that outperforms existing collections in target coverage and compact size. We also describe a mechanism of action library that optimally covers 1,852 targets in the liganded genome. Our tools facilitate creation, analysis, and updates of both private and public compound collections.

SUBMITTER: Moret N

PROVIDER: S-EPMC6526536 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries.

Moret Nienke N Clark Nicholas A NA Hafner Marc M Wang Yuan Y Lounkine Eugen E Medvedovic Mario M Wang Jinhua J Gray Nathanael N Jenkins Jeremy J Sorger Peter K PK

Cell chemical biology 20190404 5

Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery, and therapeutic repurposing. Multiple libraries are available, but few data-driven approaches exist to compare them and design new libraries. We describe an approach to scoring and creating libraries based on binding selectivity, target coverage, and induced cellular phenotypes as well as chemical structure, stage of clinical development, and user preference. The approach, available via the online to ...[more]

PMID: 30956147

Similar Datasets

Project description:BackgroundThe dynamic and differential regulation and expression of genes is majorly governed by the complex interactions of a subset of biomolecules in the cell operating at multiple levels starting from genome organisation to protein post-translational regulation. The regulatory layer contributed by the epigenetic layer has been one of the favourite areas of interest recently. This layer of regulation as we know today largely comprises of DNA modifications, histone modifications and noncoding RNA regulation and the interplay between each of these major components. Epigenetic regulation has been recently shown to be central to development of a number of disease processes. The availability of datasets of high-throughput screens for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries.MethodsIn the present study, we have used data from high throughput screens for the inhibitors of epigenetic modifiers. Computational predictive models were constructed based on the molecular descriptors. Machine learning algorithms for supervised training, Naive Bayes and Random Forest, were used to generate predictive models for the small molecule inhibitors of histone methyl-transferases and demethylases. Random forest, with the accuracy of 80%, was identified as the most accurate classifier. Further we complemented the study with substructure search approach filtering out the probable pharmacophores from the active molecules leading to drug molecules.ResultsWe show that effective use of appropriate computational algorithms could be used to learn molecular and structural correlates of biological activities of small molecules. The computational models developed could be potentially used to screen and identify potential new biological activities of molecules from large molecular libraries and prioritise them for in-depth biological assays. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding activities of small molecules inhibitors of epigenetic modifiers.

Project description:We report on the development of a cheminformatics enumeration technology and the analysis of a resulting large dataset of virtual macrolide scaffolds. Although macrolides have been shown to have valuable biological properties, there is no ready-to-screen virtual library of diverse macrolides in the public domain. Conducting molecular modeling (especially virtual screening) of these complex molecules is highly relevant as the organic synthesis of these compounds, when feasible, typically requires many synthetic steps, and thus dramatically slows the discovery of new bioactive macrolides. Herein, we introduce a cheminformatics approach and associated software that allows for designing and generating libraries of virtual macrocycle/macrolide scaffolds with user-defined constitutional and structural constraints (e.g., types and numbers of structural motifs to be included in the macrocycle, ring size, maximum number of compounds generated). To study the chemical diversity of such generated molecules, we enumerated V1M (Virtual 1 million Macrolide scaffolds) library, each containing twelve common structural motifs. For each macrolide scaffold, we calculated several key properties, such as molecular weight, hydrogen bond donors/acceptors, topological polar surface area. In this study, we discuss (1) the initial concept and current features of our PKS (polyketides) Enumerator software, (2) the chemical diversity and distribution of structural motifs in V1M library, and (3) the unique opportunities for future virtual screening of such enumerated ensembles of macrolides. Importantly, V1M is provided in the Supplementary Material of this paper allowing other researchers to conduct any type of molecular modeling and virtual screening studies. Therefore, this technology for enumerating extremely large libraries of macrolide scaffolds could hold a unique potential in the field of computational chemistry and drug discovery for rational designing of new antibiotics and anti-cancer agents.

Dataset Information

Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries.

Publications

Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets