ABSTRACT In this work we demonstrated, that machine learning opens a way for real design of ligands with required metal ion selectivity. We performed the ensemble QSPR modelling of the Li+/Na+ complexation selectivity and the stability constants for the Li+L and Na+L complexes of phosphoryl podands in nonaqueous solvent THF/СНCl3 (4:1 v/v). The models were built and cross-validated using MLR with the

ABSTRACT Avermectins have been effectively used in medicine, veterinary medicine, and agriculture as antiparasitic agents for many years. However, there are still no reliable data on the main ecotoxicological characteristics of most individual avermectins. Although many QSAR models have been proposed to describe the acute toxicity of organic compounds towards Tetrahymena pyriformis (T. pyriformis)

ABSTRACT Cyclin-dependent kinase 2 (CDK2) has been regarded as a promising drug target for anti-tumour agents. In this study, molecular dynamics (MD) simulations and principal component (PC) analysis were used to explore binding mechanism of three inhibitors 1PU, CDK, 50Z to CDK2 and influences of their bindings on conformational changes of CDK2. The results show that bindings of inhibitors yield obvious

ABSTRACT The quantitative structure‐property relationship (QSPR) method is commonly used to predict different physicochemical characteristics of interest of chemical compounds with an objective to accelerate the process of design and development of novel chemical compounds in the biotechnology and healthcare industries. In the present report, we have employed a QSPR approach to predict the different

ABSTRACT The protonation states of two aspartic acids in the catalytic strands of HIV-1 protease (PR) remarkably affect bindings of inhibitors to PR. It is requisite for the design of potent inhibitors towards PR to investigate the influences of Asp25/Asp25ʹ protonated states on dynamics behaviour of PR and binding mechanism of inhibitors to PR. In this work, molecular dynamics (MD) simulations, MM-GBSA

ABSTRACT A new subset of furan-pyrazole piperidine derivatives was used for QSAR model development. These compounds exhibit good Akt1 inhibitory activity; moreover, antiproliferative activities in vitro against OVCAR-8 (Human ovarian carcinoma cells) and HCT116 (human colon cancer cells), were confirmed for them. Based on the relevant three-dimensional (3D) and 2D autocorrelation descriptors, selected

ABSTRACT The hydrolysis of organic chemicals such as pesticides, pollutants, or drugs can affect the fate and behaviour of environmental contaminants, so it is of interest to evaluate the stability of substances in water for various purposes. For the registration of organic compounds in Europe, information on hydrolysis must be presented. However, the experimental measurements of all chemicals would

ABSTRACT COVID-19 is the most unanticipated incidence of 2020 affecting the human population worldwide. Currently, it is utmost important to produce novel small molecule anti-SARS-CoV-2 drugs urgently that can save human lives globally. Based on the earlier SARS-CoV and MERS-CoV infection along with the general characters of coronaviral replication, a number of drug molecules have been proposed. However

ABSTRACT Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC50) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target

ABSTRACT HIV-1 gp120 provides a multistage viral entry process through the conserved CD4 binding site. Hunting of potential blockers can diminish the interaction of gp120 with the CD4 host receptor leading to the suppression of HIV-1 infection. Structure-based pharmacophore virtual screening followed by binding free energy calculation, molecular dynamics (MD) simulation and density functional theory

ABSTRACT Linear and nonlinear quantitative structure–property relationship (QSPR) models were developed based on a dataset with 65 polymer-solvent combinations. Seven quantum chemical descriptors, dipole moment, hardness, chemical potential, electrophilicity index, total energy, HOMO and LUMO orbital energies, were calculated with density functional theory at the B3LYP/6-31 G(d) level for polymers

ABSTRACT The fumigant and topical activities exhibited by 27 plant-derived essentials oils (EOs) on adult M. domestica housefly are predicted through the Quantitative Structure-Activity Relationship (QSAR) theory. These molecular structure based calculations are performed on 253 structurally diverse compounds from the EOs, where the number of constituents in each essential oil mixture varies between

ABSTRACT Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and

ABSTRACT The dependence of statistical validation parameters was investigated on the size of the sample taken in fit of multivariate linear curves. We observed that R2 and related internal parameters were misleading as they overestimated the goodness-of-fit of models at small sample size. Cross-validation metrics showed correct trends. It was possible to scale the leave-one-out and the leave-many-out

ABSTRACT A library of 9-arylimino derivatives of noscapine was developed by coupling of Schiff base containing imine groups. Virtual screening using molecular docking with tubulin revealed three molecules, 12–14 that bind with high affinity. An improved predicted free energy of binding (FEB) of −5.390, −6.506 and −6.679 kcal/mol for the molecules 12–14 was found compared to noscapine (−5.135 kcal/mol)

ABSTRACT Adipocyte fatty-acid binding protein (A-FABP) plays a central role in many aspects of metabolic diseases. It is an important target in drug design for treatment of FABP-related diseases. In this study, molecular dynamics (MD) simulations followed by calculations of molecular mechanics generalized Born surface area (MM-GBSA) and principal components analysis (PCA) were implemented to decipher

ABSTRACT DNA replication is not only the basis of biological inheritance but also the most fundamental process in all living organisms. It plays a crucial role in the cell-division cycle and gene expression regulation. Hence, the accurate identification of the origin of replication sites (ORIs) has a great meaning for further understanding the regulatory mechanism of gene expression and treating genic

ABSTRACT DprE1 is a potential target of resistant tuberculosis (TB), especially multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. 2-benzoxazolinone is a closely related bioisostere of some scaffolds such as benzoxazoles, benzimidazole, benzothiazolinone, and benzothiazoles that have been previously explored against DprE1. Thus, a ligand-based quantitative pharmacophore model (AHRR

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 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 CREB binding protein (CBP) and its paralog E1A binding protein (p300) are related to the development of inflammatory diseases, cancers and other diseases, and have been potential targets for the treatment of human diseases. In this work, interaction mechanism of three small molecules E3T, E3H, and E3B with CBP was investigated by employing molecular dynamics (MD) simulations, principal component

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 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