Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r(2) value, r(2) (CV) value and r(2) prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC(50) values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r(2) prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.
In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.
A robust screening approach and a sparse quantitative structure-retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-SIS) method. Second, prediction of RIs was made using the proposed robust sparse QSRR with smoothly clipped absolute deviation (SCAD) penalty (RSQSRR). The RSQSRR model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], Y-randomization test, [Formula: see text], [Formula: see text], and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of the RSQSRR for training dataset outperform the other two used modelling methods. The RSQSRR shows the highest [Formula: see text], [Formula: see text], and [Formula: see text], and the lowest [Formula: see text]. For the test dataset, the RSQSRR shows a high external validation value ([Formula: see text]), and a low value of [Formula: see text] compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed RSQSRR is an efficient approach for modelling high dimensional QSRRs and the method is useful for the estimation of RIs of essential oils that have not been experimentally tested.
In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.
A new series of 2,4,6-trihydroxy-3-geranyl-acetophenone (tHGA) analogues were synthesized and evaluated for their lipoxygenase (LOX) inhibitory activity. Prenylated analogues 4a⁻g (half maximal inhibitory concentration (IC50) values ranging from 35 μ M to 95 μ M) did not exhibit better inhibitory activity than tHGA (3a) (IC50 value: 23.6 μ M) due to the reduction in hydrophobic interaction when the alkyl chain length was reduced. One geranylated analogue, 3d, with an IC50 value of 15.3 μ M, exhibited better LOX inhibitory activity when compared to tHGA (3a), which was in agreement with our previous findings. Kinetics study showed that the most active analogue (3e) and tHGA (3a) acted as competitive inhibitors. The combination of in silico approaches of molecular docking and molecular dynamic simulation revealed that the lipophilic nature of these analogues further enhanced the LOX inhibitory activity. Based on absorption, distribution, metabolism, excretion, and toxicity (ADMET) and toxicity prediction by komputer assisted technology (TOPKAT) analyses, all geranylated analogues (3a⁻g) showed no hepatotoxicity effect and were biodegradable, which indicated that they could be potentially safe drugs for treating inflammation.
Ceramicines are a series of limonoids which were isolated from the bark of Malaysian Chisocheton ceramicus (Meliaceae) and show various biological activities. Ceramicine B, in particular, has been reported to show a strong lipid droplet accumulation (LDA) inhibitory activity on a mouse pre-adipocyte cell line (MC3T3-G2/PA6). With the purpose of discovering compounds with stronger activity than ceramicine B, we further investigated the constituents of C. ceramicus. As a result, from the bark of C. ceramicus four new ceramicines (ceramicines M-P, 1-4) were isolated, and their structures were determined on the basis of NMR and mass spectroscopic analyses in combination with NMR chemical shift calculations. LDA inhibitory activity of 1-4 was evaluated. Compounds 1-3 showed LDA inhibitory activity, and 3 showed better selectivity than ceramicine B while showing activity at the same order of magnitude as ceramicine B. Since 3, which possess a carbonyl group at C-7, showed better selectivity than 5, which possess a 7α-OH group, while showing activity at the same order of magnitude as 5, we also investigated the effect of the substituent at C-7 by synthesizing several derivatives and evaluating their LDA inhibitory activity. Accordingly, we confirmed the importance of the presence of a 7α-OH group to the LDA inhibitory activity.
Topological indices are numerical values that can be analysed to predict the chemical properties of the molecular structure and the topological indices are computed for a graph related to groups. Meanwhile, the conjugacy class graph of is defined as a graph with a vertex set represented by the non-central conjugacy classes of . Two distinct vertices are connected if they have a common prime divisor. The main objective of this article is to find various topological indices including the Wiener index, the first Zagreb index and the second Zagreb index for the conjugacy class graph of dihedral groups of order where the dihedral group is the group of symmetries of regular polygon, which includes rotations and reflections. Many topological indices have been determined for simple and connected graphs in general but not graphs related to groups. In this article, the Wiener index and Zagreb index of conjugacy class graph of dihedral groups are generalized.
Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
Multiple separate quantitative structure-activity relationships (QSARs) models were built for the antiproliferative activity of substituted Phenyl 4-(2-Oxoimidazolidin-1-yl)-benzenesulfonates (PIB-SOs). A variety of descriptors were considered for PIB-SOs through QSAR model building. Genetic algorithm (GA), available in QSARINS, was employed to select optimum number and set of descriptors to build the multi-linear regression equations for a dataset of PIB-SOs. The best three parametric models were subjected to thorough internal and external validation along with Y-randomization using QSARINS, according to the OECD principles for QSAR model validation. The models were found to be statistically robust with high external predictivity. The best three parametric model, based on steric, 3D- and finger print descriptors, was found to have R(2)=0.91, R(2)ex=0.89, and CCCex=0.94. The CoMFA model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, gave F=229.34, R(2)CV=0.71 and R(2)=0.94. Steric repulsion, frequency of occurrence of carbon and nitrogen at topological distance of seven, and internal electronic environment of the molecule were found to have correlation with the anti-tumor activity of PIB-SOs.
A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.
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 that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.
Benzimidazole derivatives have been shown to possess sirtuin-inhibitory activity. In the continuous search for potent sirtuin inhibitors, systematic changes on the terminal benzene ring were performed on previously identified benzimidazole-based sirtuin inhibitors, to further investigate their structure-activity relationships. It was demonstrated that the sirtuin activities of these novel compounds followed the trend where meta-substituted compounds possessed markedly weaker potency than ortho- and para-substituted compounds, with the exception of halogenated substituents. Molecular docking studies were carried out to rationalize these observations. Apart from this, the methods used to synthesize the interesting compounds are also discussed.
Laccase belongs to the family of blue multi-copper oxidases and are capable of oxidizing a wide range of aromatic compounds. Laccases have industrial applications in paper pulping or bleaching and hydrocarbon bioremediation as a biocatalyst. We describe the design of a laccase with broader substrate spectrum in bioremediation. The application of evolutionary trace (ET) analysis of laccase at the ligand binding site for optimal design of the enzyme is described. In this attempt, class specific sites from ET analysis were mapped onto known crystal structure of laccase. The analysis revealed 162PHE as a critical residue in structure function relationship studies.
Coumarins are the phytochemicals, which belong to the family of benzopyrone, that display interesting pharmacological properties. Several natural, synthetic and semisynthetic coumarin derivatives have been discovered in decades for their applicability as lead structures as drugs. Coumarin based conjugates have been described as potential AChE, BuChE, MAO and β-amyloid inhibitors. Therefore, the objective of this review is to focus on the construction of these pharmacologically important coumarin analogues with anti-Alzheimer's activities, highlight their docking studies and structure-activity relationships based on their substitution pattern with respect to the selected positions on the chromen ring by emphasising on the research reports conducted in between year 1968 to 2017.
A new class of triazinoindole-bearing thiosemicarbazides (1-25) was synthesized and evaluated for α-glucosidase inhibitory potential. All synthesized analogs exhibited excellent inhibitory potential, with IC50 values ranging from 1.30 ± 0.01 to 35.80 ± 0.80 µM when compared to standard acarbose (an IC50 value of 38.60 ± 0.20 µM). Among the series, analogs 1 and 23 were found to be the most potent, with IC50 values of 1.30 ± 0.05 and 1.30 ± 0.01 µM, respectively. The structure-activity relationship (SAR) was mainly based upon bringing about different substituents on the phenyl rings. To confirm the binding interactions, a molecular docking study was performed.
Medicinal chemists have continuously shown interest in new curcuminoid derivatives, the diarylpentadienones, owing to their enhanced stability feature and easy preparation using a one-pot synthesis. Thus far, methods such as Claisen-Schmidt condensation and Julia-Kocienski olefination have been utilised for the synthesis of these compounds. Diarylpentadienones possess a high potential as a chemical source for designing and developing new and effective drugs for the treatment of diseases, including inflammation, cancer, and malaria. In brief, this review article focuses on the broad pharmacological applications and the summary of the structure-activity relationship of molecules which can be employed to further explore the structure of diarylpentadienone. The current methodological developments towards the synthesis of diarylpentadienones are also discussed.
Thiadiazole derivatives 1-24 were synthesized via a single step reaction and screened for in vitro β-glucuronidase inhibitory activity. All the synthetic compounds displayed good inhibitory activity in the range of IC50=2.16±0.01-58.06±1.60μM as compare to standard d-saccharic acid 1,4-lactone (IC50=48.4±1.25μM). Molecular docking study was conducted in order to establish the structure-activity relationship (SAR) which demonstrated that thiadiazole as well as both aryl moieties (aryl and N-aryl) involved to exhibit the inhibitory potential. All the synthetic compounds were characterized by spectroscopic techniques (1)H, (13)C NMR, and EIMS.