NaPLeS: a natural products likeness scorer—web application and database

Sorokina, Maria; Steinbeck, Christoph

doi:10.1186/s13321-019-0378-z

Cited by 42 publications

(42 citation statements)

References 18 publications

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“…Another interesting correlation to be noted is between the molecular weight and the nitrogen atom number in sugar-free molecules. The NP-likeness score [22], trained on high-quality NPs dataset and computed with NaPLeS [23], which was trained on high-quality NPs dataset, has a typical distribution for an NPs set, where most molecules have a positive score. At this point, an additional NPs curation step has been performed, due to the possible inconsistency in genuine NPs of one of the used sources, SuperNatural II.…”

Section: Natural Product Namingmentioning

confidence: 99%

COCONUT online: Collection of Open Natural Products database

et al. 2021

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Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other in silico applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at https://coconut.naturalproducts.net.

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Section: Natural Product Namingmentioning

confidence: 99%

COCONUT online: Collection of Open Natural Products database

et al. 2021

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“…The 50 NP collections from which NP structures could be downloaded were analysed in order to evaluate their overlap in terms of molecular structures and coherence of their content. 19 physicochemical properties, such as molecular weight, NP-likeness [172,173], logP, TPSA Efficiency, and Zagreb Index, were computed and their distributions are shown in an interactive graphic at https ://nprev iew.natur alpro ducts .net. Due to the high number of databases to compare, a non-interactive would not be visible.…”

Section: Comparison and Analysis Of The Content Of Open Np Databasesmentioning

confidence: 99%

Review on natural products databases: where to find data in 2020

2020

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Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.

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“…The NP-Likeness Score has been reimplemented in different software and platforms, with some modifications. [100][101][102][103] Further approaches include a conceptually related method employing extended connectivity fingerprints (ECFPs) [98] as well as a rule-based approach. [104] More recently, we developed NP-Scout, [59] a tool for identifying NPs and NP-like compounds in large sets of molecules.…”

Section: Computational Methods For the Assessment Of Natural Product-mentioning

confidence: 99%

Cheminformatics in Natural Product‐based Drug Discovery

Kirchmair

2020

Molecular Informatics

108

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This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural productsinspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

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NaPLeS: a natural products likeness scorer—web application and database

Cited by 42 publications

References 18 publications

COCONUT online: Collection of Open Natural Products database

COCONUT online: Collection of Open Natural Products database

Review on natural products databases: where to find data in 2020

Cheminformatics in Natural Product‐based Drug Discovery

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