ABSTRACT Cannabinoid receptor has been shown to be overexpressed in various types of cancers, especially non-small cell lung cancer. As a result, it could be used as novel target for anticancer treatments. Because receptor-dependent 4D-QSAR generates conformational ensemble profiles of compounds by molecular dynamics simulations at the binding site of the enzyme, this work describes the synthesis,

ABSTRACT Acetyl Coenzyme A Carboxylase (AccD6) is a homodimeric protein which is involved in the carboxylation of acetyl coenzyme A to produce malonyl coenzyme A, which plays an important role in the biosynthesis of fatty acid chain. However, studies suggest that AccD6 in combination with AccA3 produces malonyl co-A. Certain herbicides are known to inhibit plant ACC. Among these herbicides, haloxyfop

ABSTRACT Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we conducted structure-activity relationship (SAR) study on 1097 diverse mushroom tyrosinase inhibitors. We applied five kinds of machine learning methods

ABSTRACT This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption (ED): the oestrogen (ER) and the androgen (AR) receptors. The ED-relevant data were retrieved from multiple sources, including the CERAPP, CoMPARA, and the Tox21 projects as well as

ABSTRACT A series of 18 2-arylidene indan-1,3-dione derivatives was synthesized and tested against Daphnia magna to assess the environmental toxicity of these compounds. Aiming to investigate the toxicity mechanism for this series of compounds, a four-dimensional quantitative structure–activity analysis (4D-QSAR) was performed through the partial least square regression (PLS). The best PLS model was

ABSTRACT One step towards reduced animal testing is the use of in silico screening methods to predict toxicity of chemicals, which requires high-quality data to develop models that are reliable and clearly interpretable. We compiled a large data set of fish early life stage no observed effect concentration endpoints (FELS NOEC) based on published data sources and internal studies, containing data for

ABSTRACT In this article, we consider cross-validation of the quantitative structure-property relationship models for reactions and show that the conventional k-fold cross-validation (CV) procedure gives an ‘optimistically’ biased assessment of prediction performance. To address this issue, we suggest two strategies of model cross-validation, ‘transformation-out’ CV, and ‘solvent-out’ CV. Unlike the

ABSTRACT One step towards reduced animal testing is the use of in silico screening methods to predict toxicity of chemicals, which requires high-quality data to develop models that are reliable and clearly interpretable. We compiled a large data set of fish early life stage no observed effect concentration endpoints (FELS NOEC) based on published data sources and internal studies, containing data for

ABSTRACT Leishmaniasis affects mainly rural areas and the poorest people in the world. A computational study of the antileishmanial activity of organic selenium and tellurium compounds was performed. The 3D structures of the compounds were optimized at the wb97xd/lanl2dz level and used in the quantitative structure-activity relationship (QSAR) analysis. The antileishmanial activity was measured by

ABSTRACT A Förster resonance energy transfer (FRET)-based assay was used to screen the FDA-approved compound library against the MERS-CoV helicase, an essential enzyme for virus replication within the host cell. Five compounds inhibited the helicase activity with submicromolar potencies (IC50, 0.73–1.65 µM) and ten compounds inhibited the enzyme with micromolar potencies (IC50, 19.6–502 µM). The molecular

ABSTRACT Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative

ABSTRACT Interleukin (IL)-33 is a new cytokine of the IL-1 family that is related to several inflammatory and autoimmune diseases. IL-33 binds to its ST2 receptor and leads to biological responses thereof. Currently, no drugs have been approved for the treatment of IL-33 related diseases. The aim of this study was to search for small molecules that inhibit the protein–protein interaction between IL-33

ABSTRACT Cancer remains one of the leading causes of death in humans, and new drug substances are therefore being developed. Thus, the anti-cancer activity of xanthene derivatives has become an important topic in the development of new and potent anti-cancer drug substances. Previously published novel series of xanthen-3-one and xanthen-1,8-dione derivatives have been synthesized in one of our laboratories

ABSTRACT Ideal correlation is one variable model based on so-called optimal descriptors calculated with simplified molecular input-line entry systems (SMILES). The optimal descriptor is calculated according to the index of ideality of correlation, a new criterion of predictive potential of quantitative structure–property/activity relationships (QSPRs/QSARs). The aim of the present study was the building

ABSTRACT The Monte Carlo algorithm was applied to formulate a robust quantitative structure–property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data set of 309 water contaminants containing 125 aliphatic and 184 phenyl-based chemicals. The QSPR models were computed with the hybrid optimal descriptors which were procured by combining the SMILES

ABSTRACT The Monte Carlo algorithm was applied to formulate a robust quantitative structure–property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data set of 309 water contaminants containing 125 aliphatic and 184 phenyl-based chemicals. The QSPR models were computed with the hybrid optimal descriptors which were procured by combining the SMILES

ABSTRACT Ideal correlation is one variable model based on so-called optimal descriptors calculated with simplified molecular input-line entry systems (SMILES). The optimal descriptor is calculated according to the index of ideality of correlation, a new criterion of predictive potential of quantitative structure–property/activity relationships (QSPRs/QSARs). The aim of the present study was the building

ABSTRACT In the current study, the Asinex and ChEBI databases were virtually screened for the identification of potential Lyn protein inhibitors. Therefore, a multi-steps molecular docking study was carried out using the VSW utility tool embedded in Maestro user interface of the Schrödinger suite. On initial screening, molecules having a higher XP-docking score and binding free energy compared to Staurosporin

ABSTRACT 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

ABSTRACT Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the

ABSTRACT Sigma-2 (σ2) receptor is a transmembrane protein shown to be linked with neurodegenerative diseases and cancer development. Thus, it emerges as a potential biological target for the advancement of anticancer and anti-Alzheimer’s agents. The current study was aimed to identify potential σ2 receptor ligands using integrated computational approaches including homology modelling, combined pharmacophore-

ABSTRACT A novel coronavirus recently identified in Wuhan, China (2019-nCoV) has resulted in an increasing number of patients globally, and has become a highly lethal pathogenic member of the coronavirus family affecting humans. 2019-nCoV has established itself as one of the most threatening pandemics that human beings have faced, and therefore analysis and evaluation of all possible responses against

ABSTRACT Ligand-based pharmacophore modelling and virtual screening along with in vitro screening were performed as a rational strategy for the identification of novel compounds as apoptosis inducers and anticancer agents from the chemical database. Known apoptosis inducers were selected from the literature for generation of pharmacophore models, which were subjected to validation using Receiver operating

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