Quantitative Structure-activity Relationship Studies Research Articles

Quantitative Structure-Activity Relationship, Structure-based Design, and ADMET studies of pyrimethamine and cycloguanil analogs inhibitors of Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (PfDHFR-TS).

Quantitative Structure-Activity Relationship, Structure-based Design, and ADMET studies of pyrimethamine and cycloguanil analogs inhibitors of Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (PfDHFR-TS).

Quantitative Structure-Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective.

The preclinical drug discovery stage often requires a large amount of costly and time-consuming experiments using huge sets of chemical compounds. In the last few decades, this process has undergone significant improvements by the introduction of quantitative structure-activity relationship (QSAR) modelling that uses a certain percentage of experimental data to predict the biological activity/property of compounds with similar structural skeleton and/or containing a particular functional group(s). The use of machine learning tools along with it has made life even easier for pharmaceutical researchers. Here, we discuss the toxicity of certain sets of bioactive compounds towards Pimephales promelas and Tetrahymena pyriformis in terms of the global conceptual density functional theory (CDFT)-based descriptor, electrophilicity index (ω). We have compared the results with those obtained by using the commonly used hydrophobicity parameter, logP (where P is the n-octanol/water partition coefficient), considering the greater ease of computing the ω descriptor. The Human African trypanosomiasis (HAT) curing activity of 32 pyridyl benzamide derivatives is also studied against Tryphanosoma brucei. In this review article, we summarize these multiple linear regression (MLR)-based QSAR studies in terms of electrophilicity (ω, ω2) and hydrophobicity (logP, (logP)2) parameters.

Methoxy and methylthio-substituted trans-stilbene derivatives as CYP1B1 inhibitors – QSAR study with detailed interpretation of molecular descriptors

A quantitative structure–activity relationship (QSAR) model was built using multiple linear regression (MLR) to predict the ability of series methyl and/or methylthio trans -stilbene derivatives to inhibit CYP1B1. Twenty-four compounds with their activity expressed as the negative log of the IC 50 value (pIC50 [M]) were split into a training (20 compounds) and a test set (four compounds) using Kennard and Stone algorithm. Molecular descriptors were calculated using alvaDesc software after compound optimization in the Gaussian 09 package in PL-Grid. The model characterized by the best validation parameters (R 2 TRAIN = 0.954, Q 2 LOO = 0.898, R 2 TEST = 0.880) was chosen based on the chemometric method – cluster analysis. The applicability domain has been determined, indicating that the regression model can give reliable prediction. The study shows that the inhibitory activity against CYP1B1 of the methyl and/or methylthio trans -stilbene derivatives can be predicted by RDF035m, Mor10m, Eig04_AEA(bo), RDF070s, MaxDD descriptors. Finally, the paper attempts to interpret three-dimensional descriptors by assessing the impact of interatomic interactions, following the partition of molecules into fragments, on the final value of descriptors.

Discovery of rosin-based acylhydrazone derivatives as potential antifungal agents against rice Rhizoctonia solani for sustainable crop protection.

The use of fungicides to protect crops from diseases is an effective method, and novel environmentally friendly plant-derived fungicides with enhanced performance and low toxicity are urgent requirements for sustainable agriculture. Two kinds of rosin-based acylhydrazone compounds were designed and prepared. Based on the antifungal activity assessment against Rhizoctonia solani, Fusarium oxysporum, Phytophthora capsici, Sclerotinia sclerotiorum, and Botrytis cinerea, acylhydrazone derivatives containing a thiophene ring were screened and showed an inhibitory effect on rice R. solani. Among them, Compound 4n, with an electron-withdrawing group on the benzene ring structure attached to the thiophene ring, showed optimal activity, and the EC50 value was 0.981 mg L-1 , which was lower than that of carbendazim. Furthermore, it was indicated that 4n could affect the mycelial morphology, cell membrane permeability and microstructure, cause the generation of reactive oxygen species in fungal cells, and damage the nucleus and mitochondrial physiological function, resulting in the cell death of R. solani. Meanwhile, Compound 4n exhibited a better therapeutic effect on in vivo rice plants. However, the induction activity of 4n on the defense enzyme in rice leaf sheaths showed that 4n stimulates the initial resistance of rice plants by removing active oxygen, thereby protecting the cell membrane or enhancing the strength of the cell wall. Through the quantitative structure-activity relationship study, the quantitative chemical and electrostatic descriptors significantly affect the binding of 4n with the receptor, which improves its antifungal activity. This study provides a basis for exploiting potential rosin-based fungicides in promoting sustainable crop protection. © 2022 Society of Chemical Industry.

Synthesis and biological evaluation of substituted aurone derivatives as potential tyrosinase inhibitors: in vitro, kinetic, QSAR, docking and drug-likeness studies

Tyrosinase enzyme plays an essential role in melanin biosynthesis and enzymatic browning of fruits and vegetables. To discover potent tyrosinase inhibitors, the present studies were undertaken. In this context, synthetic aurone derivatives 26–50 were designed, synthesized, and structurally elucidated by various spectroscopic techniques including IR, UV, 1H- & 13C-NMR and mass spectrometry. The target compounds 26–50 were screened for their anti-tyrosinase inhibitory potential, and thus kinetic mechanism was analyzed by Lineweaver-Burk plots. All target compounds exhibited good to excellent IC50 values in the range of 7.12 ± 0.32 μM to 66.82 ± 2.44 μM. These synthesized aurone derivatives were found as potent tyrosinase inhibitors relative to the standard kojic acid (IC50 = 16.69 ± 2.81 μM) and the compound 39 inhibited tyrosinase non-competitively (Ki = 11.8 μM) by forming an enzyme-inhibitor complex. The binding modes of these molecules were ascribed through molecular docking studies against tyrosinase protein (PDB ID: 2Y9X). The quantitative structure-activity relationship studies displayed a good correlation between 26–50 structures and their anti-tyrosinase activity (IC50) with a correlation coefficient (R2) of 0.9926. The computational studies were coherent with experimental results and these ligands exhibited good binding values against tyrosinase and interacted with core residues of target protein. Moreover, the drug-likeness analysis also showed that some compounds have a linear correlation with Lipinski’s rule of five, indicating good drug-likeness and bioactivity scores for pharmacological targets. Communicated by Ramaswamy H. Sarma

Prediction of Anti-proliferation Effect of [1,2,3]Triazolo[4,5-d]pyrimidine Derivatives by Random Forest and Mix-Kernel Function SVM with PSO.

In order to predict the anti-gastric cancer effect of [1,2,3]triazolo[4,5-d]pyrimidine derivatives (1,2,3-TPD), quantitative structure-activity relationship (QSAR) studies were performed. Based on five descriptors selected from descriptors pool, four QSAR models were established by heuristic method (HM), random forest (RF), support vector machine with radial basis kernel function (RBF-SVM), and mix-kernel function support vector machine (MIX-SVM) including radial basis kernel and polynomial kernel function. Furthermore, the model built by RF explained the importance of the descriptors selected by HM. Compared with RBF-SVM, the MIX-SVM enhanced the generalization and learning ability of the constructed model simultaneously and the multi parameters optimization problem in this method was also solved by particle swarm optimization (PSO) algorithm with very low complexity and fast convergence. Besides, leave-one-out cross validation (LOO-CV) was adopted to test the robustness of the models and Q2 was used to describe the results. And the MIX-SVM model showed the best prediction ability and strongest model robustness: R2 = 0.927, Q2 = 0.916, mean square error (MSE) = 0.027 for the training set and R2 = 0.946, Q2 = 0.913, MSE = 0.023 for the test set. This study reveals five key descriptors of 1,2,3-TPD and will provide help to screen out efficient and novel drugs in the future.

Quantitative structure-activity relationship study on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines

QSAR study has been carried out on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the reciprocal hyper-detour index (descriptor Rww) and path/walk 5 Randic shape index (descriptor PW5) played a pivotal role in rationalization of CDK2 inhibition activity of titled compounds. Molecular weight (MW), mean atomic volume scaled on Carbon atom (Mv) and atomic properties such as mass and atomic Sanderson electronegativity in terms of atomic properties weighted descriptors MATS1m, MATS3e, MATS4e, GATS3e and GATS8e, certain atom centred fragments such as H attached to C0(sp3) no X attached to next C (descriptor H-046),R--CH--X (descriptor C-027) and R--CX--X (descriptor C-029) are also predominant to explain CDK2 inhibition actions of 6-substituted 2-arylaminopurines. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.

Quantitative structure-activity relationship study on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines

Quantitative structure-activity relationship study on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines

QSAR and docking studies of pyrazole analogs as antiproliferative against human colorectal adenocarcinoma cell line HT-29

In-silico quantitative structure-activity relationship (QSAR) study was performed to develop a model on a series of novel pyrazole derivatives containing acetamide moiety which exhibited considerable antiproliferative activity against human colorectal adenocarcinoma cell line HT-29. The model obtained has a correlation coefficient (r) of 0.9693, squared correlation coefficient (r2) of 0.9395 and a leave-one-out (LOO) cross-validation coefficient (Q2) value of 0.8744. The predictive power of the developed model was confirmed by the external validation which has an r2 value of 0.9488. These parameters confirm the stability and robustness of the model to predict the activity of a new designed set of 3,5-dimethyl-pyrazole derivatives (22-36), results indicated that the compounds 26, 31, 35, and 36 showed the strongest antiproliferative activity with (IC50 = 0.182, 0.172, 0.166 and 0.024 μM, respectively) against human colorectal adenocarcinoma cell line HT-29 compared to the reference vemurafenib with (IC50 = 1.52 μM). Molecular docking was performed on the new designed compounds with the human colorectal adenocarcinoma cell line 5JRQ protein. The docking results showed that compounds 26, 31, 35, and 36 have docking affinity of -8.528, -5.932, 23.017 and 18.432 kcal/mol, respectively.

Extremal pentagonal chains with respect to the Kirchhoff index

The resistance distance between any two vertices of a connected graph G is defined as the effective resistance between them in the electrical network constructed from G by replacing each edge of G with a unit resistor. The Kirchhoff index is a resistance distance-based topological index which plays an essential role in the study of quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR). In this paper, using techniques from electric network theory and graph theory, we characterize pentagonal chains with extermal Kirchhoff indices.

QSAR Studies on Nitrobenzene Derivatives using Hyperpolarizability and Conductor like Screening model as Molecular Descriptors

Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs) area, the linear polarizability, first and second order hyperpolarizability for modelling the toxicology of the nitro substituent on the benzene ring. All the molecular descriptors were performed using semi-empirical PM6 approaches. The QSAR model was developed using stepwise multiple linear regression. We found that the stable QSAR modelling of toxicology benzene derivatives used second order hyper-polarizability and COs area, which satisfied the statistical measures. The second order hyperpolarizability shows the best QSAR model. We also discovered that the nitrobenzene derivative’s substitutional functional group has a significant effect on the quantum molecular descriptors, which reflect the QSAR model.

Synthesis of Novel 2,3-Dihydro-1,5-Benzothiazepines as α-Glucosidase Inhibitors: In Vitro, In Vivo, Kinetic, SAR, Molecular Docking, and QSAR Studies.

In the present study, a series of 2,3-dihydro-1,5-benzothiazepine derivatives 1B–14B has been synthesized sand characterized by various spectroscopic techniques. The enzyme inhibitory activities of the target analogues were assessed using in vitro and in vivo mechanism-based assays. The tested compounds 1B–14B exhibited in vitro inhibitory potential against α-glucosidase with IC50 = 2.62 ± 0.16 to 10.11 ± 0.32 μM as compared to the standard drug acarbose (IC50 = 37.38 ± 1.37 μM). Kinetic studies of the most active derivatives 2B and 3B illustrated competitive inhibitions. Based on the α-glucosidase inhibitory effect, the compounds 2B, 3B, 6B, 7B, 12B, 13B, and 14B were chosen in vivo for further evaluation of antidiabetic activity in streptozotocin-induced diabetic Wistar rats. All these evaluated compounds demonstrated significant antidiabetic activity and were found to be nontoxic in nature. Moreover, the molecular docking study was performed to elucidate the binding interactions of most active analogues with the various sites of the α-glucosidase enzyme (PDB ID 3AJ7). Additionally, quantitative structure–activity relationship (QSAR) studies were performed based on the α-glucosidase inhibitory assay. The value of correlation coefficient (r) 0.9553 shows that there was a good correlation between the 1B–14B structures and selected properties. There is a correlation between the experimental and theoretical results. Thus, these novel compounds could serve as potential candidates to become leads for the development of new drugs provoking an anti-hyperglycemic effect.

The Eccentric-Distance Sum Polynomials of Graphs by Using Graph Products

The correlations between the physico-chemical properties of a chemical structure and its molecular structure-properties are used in quantitative structure-activity and property relationship studies (QSAR/QSPR) by using graph-theoretical analysis and techniques. It is well known that some structure-activity and quantitative structure-property studies, using eccentric distance sum, are better than the corresponding values obtained by using the Wiener index. In this article, we give precise expressions for the eccentric distance sum polynomial of some graph products such as join, Cartesian, lexicographic, corona and generalized hierarchical products. We implement our outcomes to calculate this polynomial for some significant families of molecular graphs in the form of the above graph products.

Hologram QSAR and Topomer CoMFA Study on Naphthyridone Derivatives as ATAD2 Bromodomain Inhibitors

Background: ATAD2 is closely related to the occurrence and proliferation of many tumors. Thus, exploring ATAD2 inhibitors is greatly significant for the prevention and treatment of tumors. In this study, the quantitative structure–activity relationship (QSAR) analyses of 57 naphthyridone derivatives were conducted using hologram quantitative structure–activity relationship (HQSAR) and topomer comparative molecular field analysis (topomer CoMFA). Method: The 57 naphthyridone derivatives were divided into the training (44 derivatives) and testing (13 derivatives) sets. HQSAR and topomer CoMFA models were obtained by applying the SYBYL-X software and validated using various validation parameters. Contribution maps from the best HQSAR model and the contour maps from the best topomer CoMFA model were analyzed. Results: The most effective HQSAR model exhibited significant cross-validated (q2 = 0.872) and non cross-validated (r2 = 0.972) correlation coefficients, and the most effective topomer CoMFA model had q2 = 0.861 and r2 = 0.962. Several external validation parameters, such as , , , , and , were used to calculate the correlation coefficients of the test set samples and validate both models. The result exhibited a powerful predictive capability. Graphical results from HQSAR and topomer CoMFA were validated by the binding mode in the crystal structure. Conclusion: The models may be beneficial to enhance the understanding of the structure–activity relationships for this class of compounds and also provide useful clues for the design of potential ATAD2 bromodomain inhibitors.

Anticancer activity and QSAR study of sulfur-containing thiourea and sulfonamide derivatives

Sulfur-containing compounds are considered as attractive pharmacophores for discovery of new drugs regarding their versatile properties to interact with various biological targets. Quantitative structure-activity relationship (QSAR) modeling is one of well-recognized in silico tools for successful drug discovery. In this work, a set of 38 sulfur-containing derivatives (Types I–VI) were evaluated for their in vitro anticancer activities against 6 cancer cell lines. In vitro findings indicated that compound 13 was the most potent cytotoxic agent toward HuCCA-1 cell line (IC50 = 14.47 μM). Compound 14 exhibited the most potent activities against 3 investigated cell lines (i.e., HepG2, A549, and MDA-MB-231: IC50 range = 1.50–16.67 μM). Compound 10 showed the best activity for MOLT-3 (IC50 = 1.20 μM) whereas compound 22 was noted for T47D (IC50 = 7.10 μM). Subsequently, six QSAR models were built using multiple linear regression (MLR) algorithm. All constructed QSAR models provided reliable predictive performance (training sets: Rtr range = 0.8301–0.9636 and RMSEtr = 0.0666–0.2680; leave-one-out cross validation sets: RCV range = 0.7628–0.9290 and RMSECV = 0.0926–0.3188). From QSAR modeling, chemical properties such as mass, polarizability, electronegativity, van der Waals volume, octanol-water partition coefficient, as well as frequency/presence of C–N, F–F, and N–N bonds in the molecule are essential key predictors for anticancer activities of the compounds. In summary, a series of promising fluoro-thiourea derivatives (10, 13, 14, 22) were suggested as potential molecules for future development as anticancer agents. Key structure-activity knowledge obtained from the QSAR modeling was suggested to be advantageous for suggesting the effective rational design of the related sulfur-containing anticancer compounds with improved bioactivities and properties.

QSAR and Drug-likeness Studies of Thiadiazole Derivatives Against Lung Cancer

This study was aimed at building a robust quantitative structure–activity relationship (QSAR) to predict the anti-proliferate activity of 1,3,4-thiadiazole derivatives against the A549 lung cancer cell lines. The semi-empirical PM7 parametrization approach was used to optimize the complete set of 1,3,4-thiadiazole derivatives and various classes of molecular descriptors have been calculated. We built models using Fisher score and the best subset selection for feature selection, and the final model was developed using the multiple linear regression technique, all in accordance with the rigorous Organization for Economic Co-operation and Development (OECD) requirements. Furthermore, various internationally agreed severe validation parameters were used to validate the model. Overall, our established model for quick prediction should be relevant to new, untested, or not yet produced compounds that fall within the applicability domain (AD) of the model. The drug-likeness properties of the 10 compounds with the greatest activity value were also calculated using Lipinski's rule properties. Keywords: QSAR, Thiadiazole derivatives, A549, PM7, OECD

On Some Topological Indices of Metal-Organic Frameworks

Topological indices are very important because they are used in the study of QSAR (Quantitative Structure–Activity Relationships) and QSPR (Quantitative Structure–Property Relationships) which are mathematical models that show the properties among physicochemical, environmental, and biological activity of a chemical structure. The chemical structure of a molecular graph is used to find the topological indices, which are widely used in theoretical chemistry, environmental chemistry, toxicology, and many others for correlation analysis. Topological indices are numerical values that helps us in measuring the physical and chemical properties of any molecule. In this article, we have calculated some degree-based topological indices of two categories of Metal-Organic Frameworks (MOFs), named as, CoBHT (cobalt butylated hydroxytoluene) and FeTPyP (Iron tetrakis pyridyl porphyrin)-CO (MOFs), also their graphical results are compared at the end.

DFT-Based Prediction of Anti-Leishmanial Activity of Carboxylates and Their Antimony(III) Complexes Against Five Leishmanial Strains

Carboxylates and their antimony(III) complexes experimentally scanned earlier for anti-leishmanial activity (IC50) against five leishmanial strains viz., L. major, L. major (Pak), L. tropica, L. mex mex, and L. donovani. These activities have been theoretically predicted by DFT method along with quantitative structure-activity relationship (QSAR) study. Molecular modeling and geometry optimization of the all the eight compounds have been performed on workspace program of CAChe Pro software of Fujitsu by opting B88-PW91 (Becke '88; Perdew & Wang '91) GGA (generalized-gradient approximation) energy functional with DZVP (double-zeta valence polarized ) basis set in DFT (Density Functional Theory). For QSAR, multiple linear regression (MLR) analysis has been performed on Project Leader Program associated with CAChe. The reliability of correlation between experimental activities and predicted activities are r2 = 0.826, r2CV = 0.426 (L. major); r2 = 0.905, r2CV = 0.507 (L. major (Pak)); r2 = 0.980, r2CV = 0.932 (L. tropica); r2 = 0.781, r2CV = 0.580 (L. mex mex) and r2 = 0.634, r2CV = 0.376 (L. donovani), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially that shows close resemblance.

Synthesis, Antifungal Activity, Cytotoxicity and QSAR Study of Camphor Derivatives.

Control of fungal phytopathogens affecting crops and woodlands is an important goal in environmental management and the maintenance of food security. This work describes the synthesis of 37 camphor derivatives, of which 27 were new compounds. Their antifungal effects on six fungi were evaluated in vitro. Compounds 3a, 4a and 5k showed strong antifungal activity against Trametes versicolor, with EC50 values of 0.43, 6.80 and 4.86 mg/L, respectively, which were better than that of tricyclazole (EC50 118.20 mg/L) and close to or better than that of carbendazim (EC50 1.20 mg/L). The most potent compound, 3a, exhibited broad-spectrum antifungal activity towards six fungi with EC50 values within the range of 0.43–40.18 mg/L. Scanning electron microscopy demonstrated that compounds 3a, 4a and 5k gave irregular growth and shriveling of the mycelia. In vitro cytotoxicity evaluation revealed that the tested camphor derivatives had mild or no cytotoxicity for LO2 and HEK293T cell lines. Quantitative structure−activity relationship (QSAR) analysis revealed that the number of F atoms, relative molecular weight, the atomic orbital electronic population and total charge on the positively charged surfaces of the molecules of camphor derivatives have effects on antifungal activity. The present study may provide a theoretical basis for a high-value use of camphor and could be helpful for the development of novel potential antifungals.

Comprehensive Evaluation and Comparison of Machine Learning Methods in QSAR Modeling of Antioxidant Tripeptides.

Due to their multiple beneficial effects, antioxidant peptides have attracted increasing interest. Currently, the screening and identification of bioactive peptides, including antioxidative peptides based on wet-chemistry methods are time-consuming and highly rely on many advanced instruments and trained personnel. Quantitative structure–activity relationship (QSAR) analysis as an in silico method can be more efficient and cost-effective. However, model performance of QSAR studies on antioxidant peptides was still poor due to limited attempts in model development approaches. The objective of this study was to compare popular machine learning methods for antioxidant activity modeling and screening of tripeptides and identify the critical amino acid features that determine the antioxidant activity. 533 numerical indices of amino acids were adopted to characterize 130 tripeptides with known antioxidant activity from the published literature, and then 7 feature selection strategies plus pairwise correlation were used to screen the most important indices for antioxidant activity and model building. 14 machine learning methods were used to build models based on the feature selection strategies, respectively. Among the 98 models, non-linear regression methods tended to perform better, and the best model with an R2Test of 0.847 and RMSETest of 0.627 for tripeptide antioxidants was obtained by combining random forest for feature selection and tree-based extreme gradient boost regression for model development. Based on the predicted antioxidant values of 7870 unknown tripeptides, potentially high antioxidant activity tripeptides all have a tyrosine, tryptophan, or cysteine residue at the C-terminal position. Furthermore, the predicted antioxidant activity of six synthesized tripeptides was confirmed through experimental determination, and for the first time, the cysteine or tyrosine residue at the C-terminal was found to be critical to the antioxidant activity based on both QSAR models and experimental observations.