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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 27391

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Dr. Marjan Vračko

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Guest Editor

National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
Interests: QSAR; environmental and human computational toxicology; quantum chemistry; chemometrical analysis of omics data

Special Issue Information

Dear Colleagues,

Our intention is to present a collection of original scientific papers and review papers that integrate different computational (in silico) approaches and experimental data for the evaluation of the toxic properties of chemicals. We understand the term “in silico approach” to encompass quantitative structure–activity relationship (QSAR) modelling, grouping methods, the modelling of receptor–ligand interaction, and the computational analysis of -omics data.

QSAR models address the relationship between chemical structures, which are described with a set of structural and physico-chemical descriptors, and biological properties expressed in the form of a mathematical function. Alternatively, the grouping techniques can be applied to build the chemical categories of chemical analogues, which form the basis for estimation of the properties. The modelling of receptor–ligand interactions provides a detailed picture of key events which may have adverse effects at cellular, tissue, organ, and organism levels (adverse outcome pathway—AOP). On the other hand, we are witnessing the explosion of -omics data emerging from genomic and proteomic research. Its focus is to identify the changes in protein expressions when organisms (cells) are exposed to xenobiotics. For the analysis of these large-scale results, diverse computation methods are necessary.

A combined approach that integrates QSAR with other methods may serve as a screening tool for chemical hazard assessment. We believe that such a collection of papers may contribute to the better understanding of biological pathways and provide a means to more critically assess the toxic effects of chemicals.

Dr. Marjan Vračko
Guest Editor

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Keywords

computer-assisted toxicity modelling
QSAR (quantitative structure–activity relationship)
grouping and categorization of chemicals
mechanisms of key events
AOP (adverse outcome pathway)
analysis of -omics data

Published Papers (6 papers)

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Research

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14 pages, 1624 KiB

Open AccessArticle

Computational Study of Drugs Targeting Nuclear Receptors

by Maša Kenda and Marija Sollner Dolenc

Molecules 2020, 25(7), 1616; https://doi.org/10.3390/molecules25071616 - 01 Apr 2020

Cited by 12 | Viewed by 3585

Abstract

Endocrine-disrupting chemicals have been shown to interfere with the endocrine system function at the level of hormone synthesis, transport, metabolism, binding, action, and elimination. They are associated with several health problems in humans: obesity, diabetes mellitus, infertility, impaired thyroid and neuroendocrine functions, neurodevelopmental problems, and cancer are among them. As drugs are chemicals humans can be frequently exposed to for longer periods of time, special emphasis should be put on their endocrine-disrupting potential. In this study, we conducted a screen of 1046 US-approved and marketed small-molecule drugs (molecular weight between 60 and 600) for estimating their endocrine-disrupting properties. Binding affinity to 12 nuclear receptors was assessed with a molecular-docking program, Endocrine Disruptome. We identified 130 drugs with a high binding affinity to a nuclear receptor that is not their pharmacological target. In a subset of drugs with predicted high binding affinities to a nuclear receptor with Endocrine Disruptome, the positive predictive value was 0.66 when evaluated with in silico results obtained with another molecular docking program, VirtualToxLab, and 0.32 when evaluated with in vitro results from the Tox21 database. Computational screening was proven useful in prioritizing drugs for in vitro testing. We suggest that the novel interactions of drugs with nuclear receptors predicted here are further investigated. Full article

(This article belongs to the Special Issue Integrated QSAR)

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16 pages, 1314 KiB

Open AccessArticle

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

by Yasunari Matsuzaka, Takuomi Hosaka, Anna Ogaito, Kouichi Yoshinari and Yoshihiro Uesawa

Molecules 2020, 25(6), 1317; https://doi.org/10.3390/molecules25061317 - 13 Mar 2020

Cited by 17 | Viewed by 4481

Abstract

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity. Full article

(This article belongs to the Special Issue Integrated QSAR)

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20 pages, 3259 KiB

Open AccessArticle

Towards an Understanding of the Mode of Action of Human Aromatase Activity for Azoles through Quantum Chemical Descriptors-Based Regression and Structure Activity Relationship Modeling Analysis

by Chayawan Chayawan, Cosimo Toma, Emilio Benfenati and Ana Y. Caballero Alfonso

Molecules 2020, 25(3), 739; https://doi.org/10.3390/molecules25030739 - 08 Feb 2020

Cited by 8 | Viewed by 4550

Abstract

Aromatase is an enzyme member of the cytochrome P450 superfamily coded by the CYP19A1 gene. Its main action is the conversion of androgens into estrogens, transforming androstenedione into estrone and testosterone into estradiol. This enzyme is present in several tissues and it has a key role in the maintenance of the balance of androgens and estrogens, and therefore in the regulation of the endocrine system. With regard to chemical safety and human health, azoles, which are used as agrochemicals and pharmaceuticals, are potential endocrine disruptors due to their agonist or antagonist interactions with the human aromatase enzyme. This theoretical study investigated the active agonist and antagonist properties of “chemical classes of azoles” to determine the relationships of azole interaction with CYP19A1, using stereochemical and electronic properties of the molecules through classification and multilinear regression (MLR) modeling. The antagonist activities for the same substituent on diazoles and triazoles vary with its chemical composition and its position and both heterocyclic systems require aromatic substituents. The triazoles require the spherical shape and diazoles have to be in proper proportion of the branching index and the number of ring systems for the inhibition. Considering the electronic aspects, triazole antagonist activity depends on the electrophilicity index that originates from interelectronic exchange interaction (ω_HF) and the LUMO energy (

E_{LUMO}^{PM 7}

), and the diazole antagonist activity originates from the penultimate orbital (

E_{HOMONL}^{PM 7}

) of diazoles. The regression models for agonist activity show that it is opposed by the static charges but favored by the delocalized charges on the diazoles and thiazoles. This study proposes that the electron penetration of azoles toward heme group decides the binding behavior and stereochemistry requirement for antagonist activity against CYP19A1 enzyme. Full article

(This article belongs to the Special Issue Integrated QSAR)

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16 pages, 3368 KiB

Open AccessFeature PaperArticle

Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem

by Benjamin Bajželj and Viktor Drgan

Molecules 2020, 25(3), 481; https://doi.org/10.3390/molecules25030481 - 23 Jan 2020

Cited by 15 | Viewed by 2501

Abstract

Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming in vitro and in vivo studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are in silico approaches, which present a cost-efficient method for toxicity prediction. The aim of our study was to explore the capabilities of counter-propagation artificial neural networks (CPANNs) for the classification of an imbalanced dataset related to idiosyncratic drug-induced liver injury and to develop a model for prediction of the hepatotoxic potential of drugs. Genetic algorithm optimization of CPANN models was used to build models for the classification of drugs into hepatotoxic and non-hepatotoxic class using molecular descriptors. For the classification of an imbalanced dataset, we modified the classical CPANN training algorithm by integrating random subsampling into the training procedure of CPANN to improve the classification ability of CPANN. According to the number of models accepted by internal validation and according to the prediction statistics on the external set, we concluded that using an imbalanced set with balanced subsampling in each learning epoch is a better approach compared to using a fixed balanced set in the case of the counter-propagation artificial neural network learning methodology. Full article

(This article belongs to the Special Issue Integrated QSAR)

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18 pages, 5692 KiB

Open AccessArticle

Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics

by Anita Rácz, Dávid Bajusz and Károly Héberger

Molecules 2019, 24(15), 2811; https://doi.org/10.3390/molecules24152811 - 01 Aug 2019

Cited by 61 | Viewed by 7350

Abstract

Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. The prediction of toxic vs. non-toxic molecules is important due to testing on living animals, which has ethical and cost drawbacks as well. The quality of classification models can be determined with several performance parameters. which often give conflicting results. In this study, we performed a multi-level comparison with the use of different performance metrics and machine learning classification methods. Well-established and standardized protocols for the machine learning tasks were used in each case. The comparison was applied to three datasets (acute and aquatic toxicities) and the robust, yet sensitive, sum of ranking differences (SRD) and analysis of variance (ANOVA) were applied for evaluation. The effect of dataset composition (balanced vs. imbalanced) and 2-class vs. multiclass classification scenarios was also studied. Most of the performance metrics are sensitive to dataset composition, especially in 2-class classification problems. The optimal machine learning algorithm also depends significantly on the composition of the dataset. Full article

(This article belongs to the Special Issue Integrated QSAR)

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Review

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21 pages, 1116 KiB

Open AccessReview

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

by Andrey A. Buglak, Anatoly V. Zherdev and Boris B. Dzantiev

Molecules 2019, 24(24), 4537; https://doi.org/10.3390/molecules24244537 - 11 Dec 2019

Cited by 38 | Viewed by 4448

Abstract

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case. Full article

(This article belongs to the Special Issue Integrated QSAR)

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