Qualitative Structure-activity Relationship Research Articles

Developmental neurotoxicity (DNT) QSAR combination prediction model establishment and structural characteristics interpretation.

With the incidence of neurodevelopmental disorders on the rise, it is imperative to screen and evaluate developmental neurotoxicity (DNT) compounds from a large number of environmental chemicals and understand their mechanisms. In this study, DNT qualitative structure-activity relationship (QSAR) study was carried out for the first time based on DNT data of mammals and structural characterization of DNT compounds was preliminarily illustrated. Five different classification algorithms and two feature selection methods were used to construct prediction models. The best model had good predictive ability on the external test set, but a small application domain (AD). Through combining of three different models, both MCC and AD values were improved. Furthermore, electronical properties, van der Waals volume-related properties and S, Cl or P containing substructure were found to be associated with DNT through modeling descriptors analysis and structure alerts (SAs) identification. This study lays a foundation for further DNT prediction of environmental exposures in human and contributes to the understanding of DNT mechanism.

Structure-Activity Assessment and In-Depth Analysis of Biased Agonism in a Set of Phenylalkylamine 5-HT2A Receptor Agonists.

Serotonergic psychedelics are described to have activation of the serotonin 2A receptor (5-HT2A) as their main pharmacological action. Despite their relevance, the molecular mechanisms underlying the psychedelic effects induced by certain 5-HT2A agonists remain elusive. One of the proposed hypotheses is the occurrence of biased agonism, defined as the preferential activation of certain signaling pathways over others. This study comparatively monitored the efficiency of a diverse panel of 4-position-substituted (and N-benzyl-derived) phenylalkylamines to induce recruitment of β-arrestin2 (βarr2) or miniGαq to the 5-HT2A, allowing us to assess structure-activity relationships and biased agonism. All test compounds exhibited agonist properties with a relatively large range of both EC50 and Emax values. Interestingly, the lipophilicity of the 2C-X phenethylamines was correlated with their efficacy in both assays but yielded a stronger correlation in the miniGαq- than in the βarr2-assay. Molecular docking suggested that accommodation of the 4-substituent of the 2C-X analogues in a hydrophobic pocket between transmembrane helices 4 and 5 of 5-HT2A may contribute to this differential effect. Aside from previously used standard conditions (lysergic acid diethylamide (LSD) as a reference agonist and a 2 h activation profile to assess a compound's activity), serotonin was included as a second reference agonist, and the compounds' activities were also assessed using the first 30 min of the activation profile. Under all assessed circumstances, the qualitative structure-activity relationships remained unchanged. Furthermore, the use of two reference agonists allowed for the estimation of both "benchmark bias" (relative to LSD) and "physiology bias" (relative to serotonin).

Computational Chemistry: QSAR of Dental Disease Causing Streptococcus mutans Using Eugenol Derivative Compounds from Clove Essential Oil

Research on the activity of eugenol derived compounds as antimicrobials using the qualitative structure-activity relationship (QSAR) model has been conducted. Molecular structure modeling was executed computationally using the ChemDraw Ultra 12.0 program package. Geometry optimization was performed using AM1 semi-empirical method to stabilize structure with the least energy level. Calculations of hydrophobic, electronic, and steric descriptors were done using Gaussian-09 and Marvin Sketch 64 softwares. Statistical calculations was conducted through multiple linear regression method using the IBM SPSS 25 program. Statistical analysis was carried out based on the statistical requirements of several equation models so that the best equation model was obtained. The analysis with the QSAR equation model obtained that the model 1 equation presented the best results.

Qualitative structure-activity relationships of aryl isoprenoid derivatives as biorational juvenoids - reweighing.

The online version contains supplementary material available at 10.1007/s42690-023-01025-3.

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †.

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.

Synthesis of 1,4-Dialkoxynaphthalene-Based Imidazolium Salts and Their Cytotoxicity in Cancer Cell Lines.

In this study, we designed and synthesized novel 1,4-dialkoxynaphthalene-2-alkyl imidazolium salt (IMS) derivatives containing both 1,4-dialkoxynaphthalene and imidazole, which are well known as pharmacophores. The cytotoxicities of these newly synthesized IMS derivatives were investigated in order to explore the possibility of using them to develop anticancer drugs. It was found that some of the new IMS derivatives showed good cytotoxic activities. In addition, an initial, qualitative structure-activity relationship is presented on the basis of observations of activity changes corresponding to structural changes.

Review on QSAR using Anticancer Drug

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology for faster, cheaper and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the Qualitative structure activity relationship, a computer-aided drug discovery process with a focus on anticancer drugs. Keywords: New drug discovery, Computer-aided drug discovery, the Qualitative structure activity relationship, Anticancer

Exiting the tunnel of uncertainty: crystal soak to validated hit.

Crystallographic fragment screens provide an efficient and effective way to identify small-molecule ligands of a crystallized protein. Due to their low molecular weight, such hits tend to have low, often unquantifiable, affinity for their target, complicating the twin challenges of validating the hits as authentic solution-phase ligands of the target and identifying the `best' hit(s) for further elaboration. In this article, approaches that address these challenges are assessed. Using retrospective analysis of a recent ATAD2 hit-identification campaign, alongside other examples of successful fragment-screening campaigns, it is suggested that hit validation and prioritization are best achieved by a `triangulation' approach in which the results of multiple available biochemical and biophysical techniques are correlated to develop qualitative structure-activity relationships (SARs). Such qualitative SARs may indeed be the only means by which to navigate a project through the tunnel of uncertainty that prevails before on-scale biophysical, biochemical and/or biological measurements become possible.

In Silico Investigations into the Selectivity of Psychoactive and New Psychoactive Substances in Monoamine Transporters.

New psychoactive substances (NPS) are a group of compounds that mimic the effects of illicit substances. A range of NPS have been shown to interact with the three main classes of monoamine transporters (DAT, NET, and SERT) to differing extents, but it is unclear why these differences arise. To aid in understanding the differences in affinity between the classes of monoamine transporters, several in silico experiments were conducted. Docking experiments showed there was no direct correlation between a range of scoring functions and experimental activity, but Spearman ranking analysis showed a significant correlation (α = 0.1) for DAT, with the affinity ΔG (0.42), αHB (0.40), GoldScore (0.40), and PLP (0.41) scoring functions, and for DAT (0.38) and SERT (0.40) using a consensus scoring approach. Qualitative structure-activity relationship (QSAR) experiments resulted in the generation of robust and predictive three-descriptor models for SERT (r 2 = 0.87, q 2 = 0.8, and test set r 2 = 0.74) and DAT (r 2 = 0.68, q 2 = 0.51, test set r 2 = 0.63). Both QSAR models described similar characteristics for binding, i.e., rigid hydrophobic molecules with a biogenic amine moiety, and were not sufficient to facilitate a deeper understanding of differences in affinity between the monoamine transporters. This contextualizes the observed promiscuity for NPS between the isoforms and highlights the difficulty in the design and development of compounds that are isoform-selective.

Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening.

An area of ongoing concern in toxicology and chemical risk assessment is endocrine disrupting chemicals (EDCs). However, thousands of legacy chemicals lack the toxicity testing required to assess their respective EDC potential, and this is where computational toxicology can play a crucial role. The US (United States) Environmental Protection Agency (EPA) has run two programs, the Collaborative Estrogen Receptor Activity Project (CERAPP) and the Collaborative Modeling Project for Receptor Activity (CoMPARA) which aim to predict estrogen and androgen activity, respectively. The US EPA solicited research groups from around the world to provide endocrine receptor activity Qualitative (or Quantitative) Structure Activity Relationship ([Q]SAR) models and then combined them to create consensus models for different toxicity endpoints. Random Forest (RF) models were developed to cover a broader range of substances with high predictive capabilities using large datasets from CERAPP and CoMPARA for estrogen and androgen activity, respectively. By utilizing simple descriptors from open-source software and large training datasets, RF models were created to expand the domain of applicability for predicting endocrine disrupting activity and help in the screening and prioritization of extensive chemical inventories. In addition, RFs were trained to conservatively predict the activity, meaning models are more likely to make false-positive predictions to minimize the number of False Negatives. This work presents twelve binary and multi-class RF models to predict binding, agonism, and antagonism for estrogen and androgen receptors. The RF models were found to have high predictive capabilities compared to other in silico modes, with some models reaching balanced accuracies of 93% while having coverage of 89%. These models are intended to be incorporated into evolving priority-setting workflows and integrated strategies to support the screening and selection of chemicals for further testing and assessment by identifying potential endocrine-disrupting substances.

Cytotoxic Evaluation, Molecular Docking, and 2D-QSAR Studies of Dihydropyrimidinone Derivatives as Potential Anticancer Agents.

The diverse pharmacological role of dihydropyrimidinone scaffold has made it to be an interesting drug target. Because of the high incidence and mortality rate of breast cancer, there is a dire need of discovering new pharmacotherapeutic agents in managing this disease. A series of twenty-two derivatives of 6-(chloromethyl)-4-(4-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (3a-3k) and ethyl 6-(chloromethyl)-4-(2-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (4a-4k) synthesized in a previous study were evaluated for their anticancer potential against breast cancer cell line. Molecular docking studies were performed to analyze the binding mode and interaction pattern of these compounds against nine breast cancer target proteins. The in vitro cell proliferation assay was performed against the breast cancer cell line MCF-7. The structure activity relationship of these compounds was further studied using QSARINS. Among nine proteins, the docking analysis revealed efficient binding of compounds 4f, 4e, 3e, 4g, and 4h against all target proteins. The in vitro cytotoxic assay revealed significant anticancer activity of compound 4f having IC50 of 2.15 μM. The compounds 4e, 3e, 4g, and 4h also showed anticancer activities with IC50 of 2.401, 2.41, 2.47 and 2.33 μM, respectively. The standard tamoxifen showed IC50 1.88 μM. The 2D qualitative structure-activity relationship (QSAR) analysis was also carried out to identify potential breast cancer targets through QSARINS. The final QSAR equation revealed good predictivity and statistical validation R2 and Q2 values for the model obtained from QSARINS was 0.98 and 0.97, respectively. The active compounds showed very good anticancer activities, and the binding analysis has revealed stable hydrogen bonding of these compounds with the target proteins. Moreover, the QSAR analysis has predicted useful information on the structural requirement of these compounds as anticancer agents with the importance of topological and autocorrelated descriptors in effecting the cancer activities.

-polynomial and deficient topological indices of identified graphs

To study the properties such as physical and chemical of compounds, the topological indices are introduced in chemical graph theory. These indices provide qualitative structure activity relationship (QSAR). Degree based topological indices are commonly used invariant in chemical graph theory. However, in this article, a new degree of vertices is introduced, called “deficiency degree”. Further, we have computed five topological indices based on the deficiency degree like “deficient first Zagreb index, deficient generalized Randić index, deficient harmonic index, deficient inverse sum index, deficient augmented Zagreb index” for identified graphs using the M -polynomial of graph.

New formula of degree distance index for some complex networks

Mathematics plays an important role in various fields, one of them is graph theory. Graphs can be used to model many types of relations and processes in many domains such as solving problems related to mathematical chemistry by using topological indices. A topological index of a graph is a number that quantifies the structure of the graph. It is used for modeling the biological and chemical properties of molecules in QSPR (Qualitative Structure-Property Relationships) and QSAR (Qualitative Structure-Activity Relationships) studies. The Degree Distance index DD(G) is one of the important topological indices. In this paper, we are going to determine DD(G) for some complex graphs like: Star vertex’s graph (SV), Star edge’s graph (SE), and Path’s graph (P).

Quantitative and Qualitative Structure Activity Relationship Study for Development of the Pet- Inhibitory Activity of 3-Nitro-2,4,6- Trihydroxy Benzamides

The aim in this paper of these work present two components analyses quantitative and qualitative which consisted in the development and evaluation of the first component quantitative and structure activity relationships (QSAR) for the prediction In fact, various compounds inhibiting photosynthesis constitute the largest class of commercial herbicides. All of those inhibitors, including ureas, triazines, bis carbamates and phenols, interrupt photosynthetic electron transport (PET) by binding to quinine-binding protein (D1-protein). Various physicochemical descriptors were used in multiple linear regressions method (MLR) to develop the theoretical models, than using a cross-validation with leave-one-out method to optimize the model as well as possible to fit with the biological data, the most common approach used to study. The second quality of the compound compared with criteria for their power, these rules Lu Propose the four basic characteristics that Lipinski has identified.

Synthetic enamine naphthoquinone derived from lawsone as cytotoxic agents assessed by in vitro and in silico evaluations

We synthesized ten enamine naphthoquinones with yields ranging from 43 to 76%. These compounds were screened for their in vitro antiproliferative activities by MTT assay against four types of human cancer cell lines: HCT116, PC3, HL60 and SNB19. The naphthoquinones bearing the picolylamine (7) and quinoline (12) moieties were the most actives (IC50 < 24 μM for all the cell lines), which were comparable or better to the values obtained for the control drugs. In silico evaluations allowed us to develop a qualitative Structure-Activity Relationship which suggest that electrostatic features, particularly the C2–C3 internuclear repulsion and the molecular dipole moment, relate to the biological response. Furthermore, Molecular Docking simulations indicate that the synthetic compounds have the potential to act as anticancer molecules by inhibiting topoisomerase-II and thymidylate synthase.

Stereoselective Access to Antimelanoma Agents by Hybridization and Dimerization of Dihydroartemisinin and Artesunic acid.

A library of five hybrids and six dimers of dihydroartemisinin and artesunic acid has been synthetized in a stereo‐controlled manner and evaluated for the anticancer activity against metastatic melanoma cell line (RPMI7951). Among novel derivatives, three artesunic acid dimers showed antimelanoma activity and cancer selectivity, being not toxic on normal human fibroblast (C3PV) cell line. Among the three dimers, the one bearing 4‐hydroxybenzyl alcohol as a spacer showed no cytotoxic effect (CC50>300 μM) and high antimelanoma activity (IC50=0.05 μM), which was two orders of magnitude higher than that of parent artesunic acid, and of the same order of commercial drug paclitaxel. In addition, this dimer showed cancer‐type selectivity towards melanoma compared to prostate (PC3) and breast (MDA‐MB‐231) tumors. The occurrence of a radical mechanism was hypothesized by DFO and EPR analyses. Qualitative structure activity relationships highlighted the role of artesunic acid scaffold in the control of toxicity and antimelanoma activity.

Computational investigation of 4,5-diphenyl-1H-pyrrole-3-carboxylic acid derivatives as B-cell lymphoma-extra large (Bcl-xL) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations

B-cell lymphoma-extra large (Bcl-xL) can inhibit apoptosis via heterodimerization with pro-apoptotic Bcl-2 family proteins, and is over-expressed in many different types of human tumors and has been regarded as a novel cancer therapeutic strategy. Due to the fact that current Bcl-xl inhibitors lack sustained effectiveness and the occurrence of some unpredictable side effects, the development of new inhibitors is necessary. In this study, computational study was applied to a series of Bcl-xL inhibitors to reveal the relationship between structure and activities through applying molecular docking, three-dimensional qualitative structure-activity relationship (3D-QSAR), and molecular dynamic (MD) simulations. A molecular docking study was performed to explore possible modes of action between inhibitors and Bcl-xL protein. Subsequently, 3D-QSAR models were generated with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the best CoMFA model, the Q2 and R2 values were computed as 0.927 and 0.999, while those were computed as 0.943 and 0.998 for the best CoMSIA model. Twenty new Bcl-xL inhibitors were designed, and all their predictive activities were improved than molecules in the dataset based on the contour maps. In addition, MD simulations were conducted to evaluate the stability of the complexes conformed by two inhibitors and Bcl-xL, and the results were consistent with those of the molecular docking and 3D-QSAR studies. Finally, binding free energy was computed through molecular mechanics performed by surface area approach (MM-GBSA), and the result congruent with the activities which indicated van der Waals as well as lipophilic energy contributing the most during the molecular with Bcl-xL protein binding. In brief, our research provided valuable information for further development of Bcl-xL inhibitors.

In silico toxicity evaluation of dioxins using structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR).

Prediction of pEC50 values of dioxins binding with the aryl hydrocarbon receptor (AhR) is of great significance for exploring how dioxins induce toxicity in human body and evaluating their environmental behaviors and risks. To reveal the factors that influence the toxicity of dioxins, provide more accurate mathematical models for predicting the pEC50 values of dioxins, and supplement the toxicity database of persistent organic pollutants, qualitative structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR) were used in this study. The research objects in this study were 60 organic compounds with pEC50 values and 162 compounds without pEC50 values, which included polychlorinated dibenzofurans (PCDFs), polychlorinated dibenzo-p-dioxins (PCDDs), and polybrominated dibenzo-p-dioxins (PBDDs). The qualitative structure-activity relationship (SAR) was performed first and concluded that halogen substitutions at any of the 2, 3, 7, and 8 sites increased the pEC50 value of the compound. Moreover, two-dimensional quantitative structure-activity relationship (2D-QSAR) models were established by employing multiple linear regression (MLR) method and artificial neural network (ANN) algorithm to investigate the factors affecting the pEC50 values of dioxins molecules. MLR was used to establish the well-understood linear model and ANN was used to establish a more accurate non-linear model. Both models have good fitting, robustness, and predictive ability. Importantly, the ability of dioxins binding to AhR is mainly determined by molecular descriptors including E1m, SM09_AEA (dm), RDF065u, F05 [Cl-Cl], and Neoplastic-80. In addition, the pEC50 values of the 162 dioxins without toxicity data were predicted by MLR and ANN models, respectively.

Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR, molecular docking and molecular dynamic simulations

Phosphopantetheine adenylyltransferase (PPAT) has been recognized as a promising target to develop novel antimicrobial agents, which is a hexameric enzyme that catalyzes the penultimate step in coenzyme A biosynthesis. In this work, molecular modeling study was performed with a series of PPAT inhibitors using molecular docking, three-dimensional qualitative structure-activity relationship (3D-QSAR) and molecular dynamic (MD) simulations to reveal the structural determinants for their bioactivities. Molecular docking study was applied to understand the binding mode of PPAT with its inhibitors. Subsequently, 3D-QSAR model was constructed to find the features required for different substituents on the scaffolds. For the best comparative molecular field analysis (CoMFA) model, the Q2 and R2 values of which were calculated as 0.702 and 0.989, while they were calculated as 0.767 and 0.983 for the best comparative molecular similarity index analysis model. The statistical data verified the significance and accuracy of our 3D-QSAR models. Furthermore, MD simulations were carried out to evaluate the stability of the receptor–ligand contacts in physiological conditions, and the results were consistent with molecular docking studies and 3D-QSAR contour map analysis. Binding free energy was calculated with molecular mechanics generalized born surface area approach, the result of which coincided well with bioactivities and demonstrated that van der Waals accounted for the largest portion. Overall, our study provided a valuable insight for further research work on the recognition of potent PPAT inhibitors.Communicated by Ramaswamy H. Sarma

Understanding Structure-Activity Relationships for Trypanosomal Cysteine Protease Inhibitors by Simulations and Free Energy Calculations.

The protozoan cysteine proteases cruzain in Trypanosoma cruzi and rhodesain in Trypanosoma brucei are therapeutic targets for Chagas disease and Human African Trypanosomiasis (HAT), respectively. A benzimidazole series was previously characterized as potent noncovalent competitive cruzain and rhodesain inhibitors with activity against trypanosomes. Common structure-activity relationships (SAR) trends and structural modifications leading to selectivity against each enzyme were described. However, some of these trends could not be understood based on the reported binding mode of lead compound 1. Therefore, we employed microsecond molecular dynamics simulations and free energy calculations to understand qualitative SAR trends and to quantitatively recapitulate them. Simulations revealed the most stable protein-ligand interactions and provided insights concerning enzyme selectivity. Calculated relative binding free energies of compound 1 analogs exhibited deviations of 1.1 and 2.2 kcal/mol from the experimental values for cruzain and rhodesain, respectively. These data encourage prospective thermodynamic integration (TI) studies to optimize this series and facilitate the prioritization of compounds for synthesis.