High-dimensionality is one of the major problems which affect the quality of the quantitative structure–activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection

Aedes aegypti is the primary vector of several infectious viruses that cause yellow, dengue, chikungunya, and Zika fevers. Recently, plant-derived products have been tested as safe and eco-friendly larvicides against Ae. aegypti. The present study aimed to improve QSAR models for 62 larvicidal phytocompounds against Ae. aegypti via the Monte Carlo method based on the index of the ideality of correlation

The human immunodeficiency virus is a lethal pathology considered as a worldwide problem. The search for new strategies for the treatment of this disease continues to be a great challenge in the scientific community. In this study, a series of 107 derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine, previously evaluated experimentally against HIV-I reverse transcriptase, was used to model

The free COOH group of conventional NSAIDs is a structural feature for non-selective cyclooxygenase (COX) inhibition and the molecular cause of their gastrointestinal (GI) toxicity. In this context, an in house database of synthesizable ester prodrugs of some well-known NSAIDs was developed by combining their -COOH group with -OH of a newly identified antioxidant 4-(1H-benzo[d]imidazol-2-yl)phenol

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model

A quantitative structure–activity relationship (QSAR) model was built from a dataset of 54 peptide-type compounds as SARS-CoV inhibitors. The analysis was executed to identify prominent and hidden structural features that govern anti-SARS-CoV activity. The QSAR model was derived from the genetic algorithm–multi-linear regression (GA-MLR) methodology. This resulted in the generation of a statistically

We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSEext = 0.56–0.78)

A set of 23 steroidal 1,2,4,5-tetraoxane analogues were studied using quantum-chemical method (B3LYP/6-31 G*) and multivariate analyses (PCA, HCA, KNN and SIMCA) in order to calculate the properties and correlate them with antimalarial activity (log RA) against Plasmodium falciparum clone D-6 from Sierra Leone. PCA results indicated 99.94% of the total variance and it was possible to divide the compounds

Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We

We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSEext = 0.56–0.78)

One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies

The n-octanol/buffer solution distribution coefficient (or n-octanol/water partition coefficient) is of critical importance for measuring lipophilicity of drug candidates. After 4885 molecular descriptor generation, 15 molecular descriptors were selected to develop quantitative structure–property relationship (QSPR) models for distribution coefficients at pH 7.4 (log D 7.4) of a large data set consisting

Here we report a new predictive model for autoignition temperature (AIT), an important physical parameter widely used to assess potential safety hazards of combustible materials. Available structure-AIT data extracted from different sources were critically analysed. Support vector regression (SVR) models on different data subsets were built in order to identify a reliable compound set on which a realistic

The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe

The evaluation of persistency of chemicals in environmental media (water, soil, sediment) is included in European Regulations, in the context of the Persistence, Bioaccumulation and Toxicity (PBT) assessment. In silico predictions are valuable alternatives for compounds screening and prioritization. However, already existing prediction tools have limitations: narrow applicability domains due to their

ABSTRACT In the context of recently emerged pandemic of COVID-19, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) modelling using SARS-CoV-3CLpro enzyme inhibitors for the development of a multiple linear regression (MLR) based model. We have used 2D descriptors with an aim to develop an easily interpretable, transferable and reproducible model which may be

Quantitative structure–property relationship (QSPR) models were developed for predicting the pungency of a set of capsaicinoids. Multiple linear regression (MLR) coupled with topological substructural molecular descriptor (TOPS-MODE) approach was used. The best MLR model based on only five orthogonalized TOPS-MODE variables allowed us to obtain a coefficient of determination of 0.954 on the training

It is well known that bromodomain-containing protein 4 (BRD4) has been thought as a promising target utilized for treating various human diseases, such as inflammatory disorders, malignant tumours, acute myelogenous leukaemia (AML), bone diseases, etc. For this study, molecular dynamics (MD) simulations, binding free energy calculations, and principal component analysis (PCA) were integrated together

Cardiovascular disease (CVD) is one of the major causes of human death. Preliminary evidence indicates that the inhibition treatment of Cholesteryl Ester Transfer Protein (CETP) causes the most pronounced increase in HDL cholesterol reported so far. Merck has disclosed certain (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]−4-methyl-1,3-oxazolidin-2-one derivatives, which show potent CETP inhibitory activity

The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the

In silico methods are often used for predicting pharmacokinetic properties of drugs due to their simplicity and cost-effectiveness. This study evaluates the penetration of 83 active pharmaceutical ingredients into human breast milk with an experimental milk-to-plasma ratio (M/P) obtained from the literature. Multiple linear regression (MLR), partial least squares (PLS) and random forest (RF) regression

Dengue fever is a disease transmitted by infected mosquitoes. This disease spreads in several countries, especially those with a tropical climate. To date, there is no specific drug that can be used to treat dengue. Use of clinically investigated drugs, such as Balapiravir, is still not effective in inhibiting the activity of virus replication. The design of a drug candidate can be performed by using

Emerging evidences indicate bromodomain-containing proteins 2 and 4 (BRD2 and BRD4) play critical roles in cancers, inflammations, cardiovascular diseases and other pathologies. Multiple short molecular dynamics (MSMD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were applied to investigate the binding selectivity of three inhibitors 87D, 88M and 89G

Two simple and reliable correlations are introduced for the prediction of emission and absorption of porphyrins and their derivatives, i.e. metalloporphyrins and ligand coordinated metalloporphyrins. They can be used to sense the extracted precious metals. The proposed models require only simple structural parameters such as the number of carbon, metal and metal-free molecular fragments of desirable

Pim kinase enzyme has an essential role in the treatment of prostate, colon and acute myeloid leukaemia cancers. The indoles inhibitors were docked in the enzyme’s active pocket in order to survey the inhibition mechanism and extract the ligands’ conformations. The docking outcome shows that the active inhibitors have strong van der Waals interactions with residues of Ile185, Leu44, Leu120 and Leu174

(2020). Correction. SAR and QSAR in Environmental Research: Vol. 31, No. 4, pp. i-i.

Support vector machine (SVM) and general regression neural network (GRNN) were used to develop classification models for predicting the antimalarial activity against Plasmodium falciparum. Only 15 molecular descriptors were used to build the classification models for the antimalarial activities of 4750 compounds, which were divided into a training set (3887 compounds) and a test set (863 compounds)

Several 3D-QSAR models were built based on 196 hepatitis C virus (HCV) NS5A protein inhibitors. The bioactivity values EC90 for three types of inhibitors, the wild type (GT1a) and two mutants (GT1a Y93H and GT1a L31V), were collected to build three datasets. The programs OMEGA and ROCS were used for generating conformations and aligning molecules of the dataset, respectively. Each dataset was randomly

A method for combining statistical-based QSAR predictions of two or more binary classification models is presented. It was assumed that all models were independent. This facilitated the combination of positive and negative predictions using a quantitative weight of evidence (qWoE) procedure based on Bayesian statistics and the additivity of the logarithms of the likelihood ratios. Previous studies

In continuation of our earlier work (Doi: 10.1080/07391102.2019.1661876), a statistically validated and robust Bayesian model was developed on a large diverse set of HDAC8 inhibitors. The training set comprised of 676 small molecules and 293 compounds were considered as test set molecules. The findings of this analysis will help to explore some major directions regarding the HDAC8 inhibitor designing

(2020). Correction. SAR and QSAR in Environmental Research: Vol. 31, No. 5, pp. 421-421.

The European Registration, Evaluation, Authorization and Restriction of Chemical Substances Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB), considering in silico prediction as valid alternative to experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain

In this study we consider relatively new bond-additive Mostar indices that appear to provide quantitative measures of peripheral shapes of molecules. We have computed weighted Mostar, edge-Mostar and total-Mostar indices of graphene, [Formula: see text]-types of graphyne and graphdiyne, which are of considerable interest owing to their novel properties and thus find applications in a number of areas

The aryl hydrocarbon receptor (AhR) plays an important role in several biological processes such as reproduction, immunity and homoeostasis. However, little is known on the chemical-structural and physicochemical features that influence the activity of AhR antagonistic modulators. In the present report, in vitro AhR antagonistic activity evaluations, based on a chemical-activated luciferase gene expression

Biocides are multi-component products used to control undesired and harmful organisms able to affect human or animal health or to damage natural and manufactured products. Because of their widespread use, aquatic and terrestrial ecosystems could be contaminated by biocides. The environmental impact of biocides is evaluated through eco-toxicological studies with model organisms of terrestrial and aquatic

ABSTRACT The zinc-dependent enzyme aminopeptidase N (APN) is a member of the M1 metalloenzyme family. The multi-functionality of APN as a peptidase, a receptor and a signalling molecule has provided it the access to influence a number of disease conditions namely viral diseases, angiogenesis, cellular metastasis and invasion including different cancer conditions. Hence, the development of potent APN

Folates are essential biomolecules required to carry out many crucial processes in leishmania parasite. Dihydrofolate reductase-thymidylate synthase (DHFR-TS) and pteridine reductase 1 (PTR1) involved in folate biosynthesis in leishmania have been established as suitable targets for development of chemotherapy against leishmaniasis. In the present study, various computational tools such as homology

Thermal conductivity is an essential thermodynamic property in chemical engineering application. As a result, estimating the thermal conductivity of organic compounds is of significance in industry production. Alkyl halides are important organic intermediates and raw materials, but little investigations have been performed to estimate their thermal conductivity. In this study, the structures of compounds

Ecological risk assessment is challenged by the need to assess hazard to the diverse communities of organisms inhabiting aquatic and terrestrial systems. Computational approaches, such as Quantitative Structure Activity Relationships (QSAR) and Interspecies Correlation Estimation (ICE) models, are useful tools that provide estimates of acute toxicity where data are lacking or limited for ecological

In the present work, an existing vegetation/air/litter/soil model (SoilPlusVeg) was modified to improve organic chemical fate description in terrestrial/aquatic ecosystems accounting for horizontal and vertical particulate organic carbon (POC) transport in soil. The model was applied to simulate the fate of three pesticides (terbuthylazine, chlorpyrifos and etofenprox), characterized by increasing

We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725-742). In addition to the descriptors from the previous study, we

Mycobacterium tuberculosis DNA gyrase subunit B (GyrB) has been identified as a promising target for rational drug design against fluoroquinolone drug-resistant tuberculosis. In this study, we attempted to identify the key structural feature for highly potent GyrB inhibitors through 2D-QSAR using HQSAR, 3D-QSAR using CoMSIA and molecular dynamics (MD) simulations approaches on a series of thiazole

Influenza A virus (IAV) has caused epidemic infections worldwide, with many strains resistant to inhibitors of a surface protein, neuraminidase (NA), due to point mutations on its structure. A novel NA inhibitor named peramivir was recently approved, but no exhaustive computational research regarding its binding affinity with wild-type and mutant NA has been conducted. In this study, a thorough investigation

We report predictive models of acute oral systemic toxicity representing a follow-up of our previous work in the framework of the NICEATM project. It includes the update of original models through the addition of new data and an external validation of the models using a dataset relevant for the chemical industry context. A regression model for LD50 and multi-class classification model for toxicity

DNA methylations are carried out by DNA methyltransferases (DNMTs) that are key enzymes during gene expression. Many chemicals, including pesticides, have shown modulation of epigenetic functions by inhibiting DNMTs. In this work, human DNMTs were evaluated as a potential target for pesticides through virtual screening of 1038 pesticides on DNMT1 (3SWR) and DNMT3A (2QRV). Molecular docking calculations

Graphene oxide is most often chosen as an alternative to graphene in the applications of carbon-based nanomaterials where adsorption is the primary process. However, its adsorption properties are poorly understood. The existing reports on the adsorption mechanism of graphene oxide rely on the linear free-energy/solvation-energy relationship (LFER/LSER) models. This computational work explores the role

Human malaria is the most widespread mosquito-borne life-threatening disease worldwide. In the absence of effective vaccines, prevention and treatment of malaria only depend on prophylaxis and drug-based therapy either in monotherapy or in combination. Unfortunately, the number of available antimalarial drugs presenting different mechanisms of action is rather limited. In addition, the appearance of

Bacillus anthracis is considered as a biological warfare agent because it is the causative agent of the serious infectious anthrax disease. Delay in treatment leads to lethal factor-mediated toxaemia which is very critical due to lack of therapeutic options. Consequently, attempts have been made to discover potent lethal factor (LF) protease inhibitors such as small-molecule synthetic 2-thio-1,3-thiazolidine-4-one

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and

Docking represents one of the most popular computational approaches in drug design. It has reached popularity owing to capability of identifying correct conformations of a ligand within an active site of the target-protein and of estimating the binding affinity of a ligand that is immensely helpful in prediction of compound activity. Despite many success stories, there are challenges, in particular

Nonnucleoside reverse transcriptase inhibitors (NNRTIs) remain the most promising anti-AIDS agents that target the HIV-1 reverse transcriptase enzyme (RT). However, the efficiency of approved NNRTI drugs has decreased by the appearance of drug-resistant viruses and side effects upon long-term usage. Thus, there is an urgent need for developing new, potent NNRTIs with broad spectrum against HIV-1 virus

Metabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we

Current guidance for the estimation of dermal absorption (DA) of pesticides recommends the use of default values, read-across of information between formulations and in vitro testing. While QSARs exist to estimate percutaneous absorption, their use is currently not encouraged. Therefore, the potential of publicly available models for DA estimation was investigated based on data from 564 human in vitro

Simultaneous use of the drugs may lead to undesirable Drug-Drug Interactions (DDIs) in the human body. Many DDIs are associated with changes in drug metabolism that performed by Drug-Metabolizing Enzymes (DMEs). In this case, DDI manifests itself as a result of the effect of one drug on the biotransformation of other drug(s), its slowing down (in the case of inhibiting DME) or acceleration (in case

The present study provides robust consensus quantitative structure-activity relationship (QSAR) models developed from 334 organic chemicals covering a wide chemical domain for the prediction of effective concentrations of chemicals for 50% and 10% inhibition of algal growth. Only 2D descriptors with definite physicochemical meaning were employed for QSAR model building, whereas development, validation

The rivality index (RI) is a normalized distance measurement between a molecule and their first nearest neighbours providing a robust prediction of the activity of a molecule based on the known activity of their nearest neighbours. Negative values of the RI describe molecules that would be correctly classified by a statistic algorithm and, vice versa, positive values of this index describe those molecules

Fraction Lipophilicity Index (FLI) was developed as a metric for assessing drug likeness of ionizable oral drugs. Considering that both log P and log D have distinct roles in drug action, the metric FLI allocates lipophilicity to a pH dependent neutral fraction of the molecule and is a weighted combination of log P and log D. It is expressed by equation: FLI = 2 x log P-│log D│. A dataset of 368 basic

Assessing chemical toxicity is a multidisciplinary process, traditionally involving in vivo, in vitro and in silico tests. Currently, toxicological goal is to reduce new tests on chemicals, exploiting all information yet available. Recent advancements in machine learning and deep neural networks allow computers to automatically mine patterns and learn from data. This technology, applied to (Q)SAR model

Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II

The large collection of known and experimentally verified compounds from the ChEMBL database was used to build different classification models for predicting the antimalarial activity against Plasmodium falciparum. Four different machine learning methods, namely the support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN) and XGBoost have been used for the development of models using