Not Drug-Like, but Like Drugs: Cnidaria Natural Products

Phylum Cnidaria has been an excellent source of natural products, with thousands of metabolites identified. Many of these have not been screened in bioassays. The aim of this study was to explore the potential of 5600 Cnidaria natural products (after excluding those known to derive from microbial symbionts), using a systematic approach based on chemical space, drug-likeness, predicted toxicity, and virtual screens. Previous drug-likeness measures: the rule-of-five, quantitative estimate of drug-likeness (QED), and relative drug likelihoods (RDL) are based on a relatively small number of molecular properties. We augmented this approach using reference drug and toxin data sets defined for 51 predicted molecular properties. Cnidaria natural products overlap with drugs and toxins in this chemical space, although a multivariate test suggests that there are some differences between the groups. In terms of the established drug-likeness measures, Cnidaria natural products have generally lower QED and RDL scores than drugs, with a higher prevalence of metabolites that exceed at least one rule-of-five threshold. An index of drug-likeness that includes predicted toxicity (ADMET-score), however, found that Cnidaria natural products were more favourable than drugs. A measure of the distance of individual Cnidaria natural products to the centre of the drug distribution in multivariate chemical space was related to RDL, ADMET-score, and the number of rule-of-five exceptions. This multivariate similarity measure was negatively correlated with the QED score for the same metabolite, suggesting that the different approaches capture different aspects of the drug-likeness of individual metabolites. The contrasting of different drug similarity measures can help summarise the range of drug potential in the Cnidaria natural product data set. The most favourable metabolites were around 210–265 Da, quite often sesquiterpenes, with a moderate degree of complexity. Virtual screening against cancer-relevant targets found wide evidence of affinities, with Glide scores <−7 in 19% of the Cnidaria natural products.

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A general approach for retrosynthetic molecular core analysis

Journal of Cheminformatics ◽

10.1186/s13321-019-0380-5 ◽

2019 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

J. Jesús Naveja ◽

B. Angélica Pilón-Jiménez ◽

Jürgen Bajorath ◽

José L. Medina-Franco

Keyword(s):

Single Molecule ◽

Chemical Space ◽

Significant Proportion ◽

Bipartite Network ◽

Data Sets ◽

Structure Property ◽

Data Set ◽

Chemical Structures ◽

Structure Property Relationships ◽

Analog Series

Abstract Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied “single molecule–single scaffold” correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure–property relationships, and identification of analog series and ASBS. The molecule–core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure–property relationships analyses.

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Ring-opening functionalizations of unstrained cyclic amines enabled by difluorocarbene transfer

Nature Communications ◽

10.1038/s41467-020-18557-8 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Youyoung Kim ◽

Joon Heo ◽

Dongwook Kim ◽

Sukbok Chang ◽

Sangwon Seo

Keyword(s):

Natural Products ◽

Chemical Synthesis ◽

Ring Opening ◽

Chemical Space ◽

Molecular Properties ◽

Cyclic Amines ◽

Highly Efficient ◽

Practical Strategy ◽

Selective Ring Opening ◽

Unique Chemical

Abstract Chemical synthesis based on the skeletal variation has been prolifically utilized as an attractive approach for modification of molecular properties. Given the ubiquity of unstrained cyclic amines, the ability to directly alter such motifs would grant an efficient platform to access unique chemical space. Here, we report a highly efficient and practical strategy that enables the selective ring-opening functionalization of unstrained cyclic amines. The use of difluorocarbene leads to a wide variety of multifaceted acyclic architectures, which can be further diversified to a range of distinctive homologative cyclic scaffolds. The virtue of this deconstructive strategy is demonstrated by successful modification of several natural products and pharmaceutical analogues.

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Comprehensive machine learning based study of the chemical space of herbicides

Scientific Reports ◽

10.1038/s41598-021-90690-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Davor Oršolić ◽

Vesna Pehar ◽

Tomislav Šmuc ◽

Višnja Stepanić

Keyword(s):

Natural Products ◽

Predictive Models ◽

Chemical Space ◽

Data Set ◽

Modes Of Action ◽

Molecular Features ◽

Global Increase ◽

Screening Platform ◽

Weed Resistance

AbstractWidespread use of herbicides results in the global increase in weed resistance. The rotational use of herbicides according to their modes of action (MoAs) and discovery of novel phytotoxic molecules are the two strategies used against the weed resistance. Herein, Random Forest modeling was used to build predictive models and establish comprehensive characterization of structure–activity relationships underlying herbicide classifications according to their MoAs and weed selectivity. By combining the predictive models with herbicide-likeness rules defined by selected molecular features (numbers of H-bond acceptors and donors, logP, topological and relative polar surface area, and net charge), the virtual stepwise screening platform is proposed for characterization of small weight molecules for their phytotoxic properties. The screening cascade was applied on the data set of phytotoxic natural products. The obtained results may be valuable for refinement of herbicide rotational program as well as for discovery of novel herbicides primarily among natural products as a source for molecules of novel structures and novel modes of action and translocation profiles as compared with the synthetic compounds.

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Non-Covalent Interactions Atlas Benchmark Data Sets 2: Hydrogen Bonding in an Extended Chemical Space

10.26434/chemrxiv.12587537.v1 ◽

2020 ◽

Author(s):

Jan Řezáč

Keyword(s):

Hydrogen Bonding ◽

Organic Molecules ◽

Chemical Space ◽

Data Sets ◽

Data Set ◽

Benchmark Data ◽

Non Covalent Interactions ◽

Dissociation Curves ◽

New Generation ◽

Covalent Interactions

The Non-Covalent Interactions Atlas (www.nciatlas.org) aims to provide a new generation of benchmark data sets for non-covalent interactions. The HB300SPX data set presented here extends the coverage of hydrogen bonds to phosphorus, sulfur and halogens up to iodine. It is again complemented by a set of dissociation curves, HB300SPX×10. The new data make it possible to analyze the transferability of the parametrization of e.g. dispersion corrections for DFT from simple organic molecules to a broader chemical space. The HB300SPX×10 has also been used for the extension of the parametrization of hydrogen-bonding corrections in the semiempirical PM6-D3H4X and DFTB3-D3H5 methods to additional elements.<br>

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Non-Covalent Interactions Atlas Benchmark Data Sets 5: London Dispersion in an Extended Chemical Space

10.26434/chemrxiv-2022-pl3r8 ◽

2022 ◽

Author(s):

Jan Řezáč

Keyword(s):

Chemical Space ◽

Data Sets ◽

Dft Methods ◽

Data Set ◽

Data Points ◽

Comprehensive Test ◽

Non Covalent Interactions ◽

London Dispersion ◽

Dissociation Curves ◽

Covalent Interactions

The Non-Covalent Interactions Atlas (www.nciatlas.org) has been extended with two data sets of benchmark interaction energies in complexes dominated by London dispersion. The D1200 data set of equilibrium geometries provides a thorough sampling of an extended chemical space, while the D442×10 set features dissociation curves for selected complexes. In total, they provide 5,178 new CCSD(T)/CBS data points of the highest quality. The new data have been combined with previous NCIA data sets in a comprehensive test of dispersion-corrected DFT methods, identifying the ones that achieve high accuracy in all types of non-covalent interactions in a broad chemical space. Additional tests of dispersion-corrected MP2 and semiempirical QM methods are also reported.

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Cheminformatics to Characterize Pharmacologically Active Natural Products

Biomolecules ◽

10.3390/biom10111566 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1566 ◽

Cited By ~ 1

Author(s):

José L. Medina-Franco ◽

Fernanda I. Saldívar-González

Keyword(s):

Natural Products ◽

Drug Discovery ◽

Natural Product ◽

Chemical Space ◽

Data Sets ◽

Fragment Analysis ◽

Chemical Structures ◽

Speed Up ◽

Pharmacologically Active ◽

Design Experiments

Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

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Design of chemical space networks incorporating compound distance relationships

F1000Research ◽

10.12688/f1000research.10021.2 ◽

2016 ◽

Vol 5 ◽

pp. 2634 ◽

Cited By ~ 1

Author(s):

Antonio de la Vega de León ◽

Jürgen Bajorath

Keyword(s):

Chemical Space ◽

Similarity Measures ◽

Data Sets ◽

Threshold Values ◽

Similarity Threshold ◽

Space Networks ◽

Structural Relationships ◽

Chemical Space Networks ◽

Similarity Relationships ◽

Activity Information

Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no chemical meaning. By contrast, inter-compound distances as a measure of dissimilarity can be directly obtained from coordinate-based representations of chemical space. Herein, we introduce a CSN variant that incorporates compound distance relationships and thus further increases the information content of compound networks. The design was facilitated by adapting the Kamada-Kawai algorithm. Kamada-Kawai networks are the first CSNs that are based on numerical similarity measures, but do not depend on chosen similarity threshold values.

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Design of chemical space networks incorporating compound distance relationships

F1000Research ◽

10.12688/f1000research.10021.1 ◽

2016 ◽

Vol 5 ◽

pp. 2634

Author(s):

Antonio de la Vega de León ◽

Jürgen Bajorath

Keyword(s):

Chemical Space ◽

Similarity Measures ◽

Data Sets ◽

Threshold Values ◽

Similarity Threshold ◽

Space Networks ◽

Structural Relationships ◽

Chemical Space Networks ◽

Similarity Relationships ◽

Activity Information

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Similarity Measures for Learning in Lattice Based Biomimetic Neural Networks

Mathematics ◽

10.3390/math8091439 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1439

Author(s):

Gerhard X. Ritter ◽

Gonzalo Urcid ◽

Luis-David Lara-Rodríguez

Keyword(s):

Neural Network ◽

Similarity Measures ◽

Classification Performance ◽

Data Sets ◽

Neural Net ◽

Real World Data ◽

Data Set ◽

Test Input ◽

Dendritic Network ◽

Pattern Application

This paper presents a novel lattice based biomimetic neural network trained by means of a similarity measure derived from a lattice positive valuation. For a wide class of pattern recognition problems, the proposed artificial neural network, implemented as a dendritic hetero-associative memory delivers high percentages of successful classification. The memory is a feedforward dendritic network whose arithmetical operations are based on lattice algebra and can be applied to real multivalued inputs. In this approach, the realization of recognition tasks, shows the inherent capability of prototype-class pattern associations in a fast and straightforward manner without need of any iterative scheme subject to issues about convergence. Using an artificially designed data set we show how the proposed trained neural net classifies a test input pattern. Application to a few typical real-world data sets illustrate the overall network classification performance using different training and testing sample subsets generated randomly.

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On Similarity Measures for Stochastic and Statistical Modeling

Mathematics ◽

10.3390/math9080840 ◽

2021 ◽

Vol 9 (8) ◽

pp. 840

Author(s):

Konstantinos Makris ◽

Ilia Vonta ◽

Alex Karagrigoriou

Keyword(s):

Time Series ◽

Statistical Modeling ◽

Similarity Measures ◽

Epidemiological Data ◽

Time Dependent ◽

Dependent Data ◽

Data Sets ◽

Data Set ◽

Time Points ◽

Statistical Functions

In this work, our goal is to present and discuss similarity techniques for ordered observations between time series and non-time dependent data. The purpose of the study was to measure whether ordered observations of data sets are displayed at or close to, the same time points for the case of time series and with the same or similar frequencies for the case of non-time dependent data sets. A simultaneous time pairing and comparison can be achieved effectively via indices, advanced indices and the associated index matrices based on statistical functions of ordered observations. Hence, in this work we review some previously defined standard indices and propose new advanced dimensionless indices and the associated index matrices which are both easily interpreted and provide efficient comparison of the series involved. Furthermore, the proposed methodology allows the analysis of data with different units of measurement as the indices presented are dimensionless. The applicability of the proposed methodology is explored through an epidemiological data set on influenza-like-illness (ILI). We finally provide a thorough discussion on all parameters involved in the proposed indices for practical purposes along with examples.

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