scholarly journals Comprehensive machine learning based study of the chemical space of herbicides

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.

scholarly journals BIOFACQUIM: A Mexican Compound Database of Natural Products

Biomolecules ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 31 ◽  
Author(s):  
B. Pilón-Jiménez ◽  
Fernanda Saldívar-González ◽  
Bárbara Díaz-Eufracio ◽  
José Medina-Franco
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Physicochemical Properties ◽  
Chemical Space ◽  
Structural Diversity ◽  
Proof Of Concept ◽  
Molecular Fingerprints ◽  
The Public ◽  
Compound Database

Compound databases of natural products have a major impact on drug discovery projects and other areas of research. The number of databases in the public domain with compounds with natural origins is increasing. Several countries, Brazil, France, Panama and, recently, Vietnam, have initiatives in place to construct and maintain compound databases that are representative of their diversity. In this proof-of-concept study, we discuss the first version of BIOFACQUIM, a novel compound database with natural products isolated and characterized in Mexico. We discuss its construction, curation, and a complete chemoinformatic characterization of the content and coverage in chemical space. The profile of physicochemical properties, scaffold content, and diversity, as well as structural diversity based on molecular fingerprints is reported. BIOFACQUIM is available for free.

scholarly journals Biological targets and mechanisms of action of natural products from marine cyanobacteria

2015 ◽  
Vol 32 (3) ◽  
pp. 478-503 ◽  
Author(s):  
Lilibeth A. Salvador-Reyes ◽  
Hendrik Luesch
Keyword(s):  
Natural Products ◽  
Mechanisms Of Action ◽  
Marine Cyanobacteria ◽  
Biological Targets ◽  
Screening Platform

Diversification of the screening platform coupled with innovative adaptation to natural products derived from marine cyanobacteria led to the characterization of intriguing mechanisms of action and a powerful display of potent bioactivities.

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

Marine Drugs ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 42
Author(s):  
Claire Laguionie-Marchais ◽  
A. Louise Allcock ◽  
Bill J. Baker ◽  
Ellie-Ann Conneely ◽  
Sarah G. Dietrick ◽  
...  
Keyword(s):  
Natural Products ◽  
Chemical Space ◽  
Drug Distribution ◽  
Similarity Measures ◽  
Molecular Properties ◽  
Data Sets ◽  
Moderate Degree ◽  
Data Set ◽  
Reference Drug ◽  
Microbial Symbionts

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.

Characterization of the Chemical Space of Known and Readily Obtainable Natural Products

2018 ◽  
Vol 58 (8) ◽  
pp. 1518-1532 ◽  
Author(s):  
Ya Chen ◽  
Marina Garcia de Lomana ◽  
Nils-Ole Friedrich ◽  
Johannes Kirchmair
Keyword(s):  
Natural Products ◽  
Chemical Space

scholarly journals BIOFACQUIM: A Mexican Compound Database of Natural Products

2018 ◽  
Author(s):  
B. Angélica Pilón-Jiménez ◽  
Fernanda I. Saldívar-González ◽  
Bárbara I. Díaz-Eufracio ◽  
José L. Medina-Franco
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Physicochemical Properties ◽  
Chemical Space ◽  
Structural Diversity ◽  
Molecular Fingerprints ◽  
Natural Origin ◽  
The Public ◽  
Compound Database

Compound databases of natural products have a major impact on drug discovery projects and other areas of research. The number of databases in the public domain with compounds from natural origin is increasing. Several countries have initiatives in place to construct and maintain compound databases that are representative of their diversity. Examples are Brazil, France, Panama and recently Vietnam. Herein, we discuss the first version of BIOFACQUIM, a novel compound database with natural products isolated and characterized in Mexico. We discuss its construction, curation, and a complete chemoinformatic characterization of the content and coverage in chemical space. It is reported the profile of physicochemical properties, scaffold content, and diversity, as well as structural diversity based on molecular fingerprints. BIOFACQUIM is freely available.

NMR characterization of complex natural products: Assigning novel, proton-deficient alkaloid scaffolds

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
KR Gustafson ◽  
STS Chan ◽  
D Milanowski
Keyword(s):  
Natural Products ◽  
Nmr Characterization

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

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