A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.
A linear quantitative structure activity relationship (QSAR) model is presented for modeling and predicting the inhibition of HIV-1 integrase. The model was produced by using the stepwise multiple linear regression technique on a database that consists of 67 recently discovered 1,3,4-oxadiazole substituted naphthyridine derivatives. The developed QSAR model was evaluated for statistical significance and predictive power. The key conclusion of this study is that valence connectivity index order 1, lowest unoccupied molecular orbital and dielectric energy significantly affect the inhibition of HIV-1 integrase activity by 1,3,4-oxadiazole substituted naphthyridine derivatives. The selected physicochemical descriptors serve as a first guideline for the design of novel and potent antagonists of HIV-1 integrase.
Peroxisome Proliferator-Activated Receptor γ (PPARγ) activators have drawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize new PPARγ activators. With this in mind, we explored the pharmacophoric space of PPARγ using seven diverse sets of activators. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing self-consistent and predictive quantitative structure-activity relationship (QSAR) (r2(71)=0.80, F=270.3, r2LOO=0.73, r2PRESS against 17 external test inhibitors=0.67). Three orthogonal pharmacophores emerged in the QSAR equation and were validated by receiver operating characteristic (ROC) curves analysis. The models were then used to screen the national cancer institute (NCI) list of compounds. The highest-ranking hits were tested in vitro. The most potent hits illustrated EC50 values of 15 and 224 nM.
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.
A new mathematical model has been developed that expresses the toxicities (EC₅₀ values) of a wide variety of ionic liquids (ILs) towards the freshwater flea Daphnia magna by means of a quantitative structure-activity relationship (QSAR). The data were analyzed using summed contributions from the cations, their alkyl substituents and anions. The model employed multiple linear regression analysis with polynomial model using the MATLAB software. The model predicted IL toxicities with R²=0.974 and standard error of estimate of 0.028. This model affords a practical, cost-effective and convenient alternative to experimental ecotoxicological assessment of many ILs.
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.
A linear quantitative structure activity relationship (QSAR) model is presented for predicting human immunodeficiency virus-1 (HIV-1) reverse transcriptase enzyme inhibition. The 2D QSAR and 3D-QSAR models were developed by stepwise multiple linear regression, partial least square (PLS) regression and k-nearest neighbor-molecular field analysis, PLS regression, respectively using a database consisting of 33 recently discovered benzoxazinones. The primary findings of this study is that the number of hydrogen atoms, number of (-NH2) group connected with solitary single bond alters the inhibition of HIV-1 reverse transcriptase. Further, presence of electrostatic, hydrophobic and steric field descriptors significantly affects the ability of benzoxazinone derivatives to inhibit HIV-1 reverse transcriptase. The selected descriptors could serve as a primer for the design of novel and potent antagonists of HIV-1 reverse transcriptase.
Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.
Benzyl-o-vanillin and benzimidazole nucleus serve as important pharmacophore in drug discovery. The benzyl vanillin (2-(benzyloxy)-3-methoxybenzaldehyde) compound shows anti-proliferative activity in HL60 leukemia cancer cells and can effect cell cycle progression at G2/M phase. Its apoptosis activity was due to disruption of mitochondrial functioning. In this study, we have studied a series of compounds consisting of benzyl vanillin and benzimidazole structures. We hypothesize that by fusing these two structures we can produce compounds that have better anticancer activity with improved specificity particularly towards the leukemia cell line. Here we explored the anticancer activity of three compounds namely 2-(2-benzyloxy-3-methoxyphenyl)-1H-benzimidazole, 2MP, N-1-(2-benzyloxy-3-methoxybenzyl)-2-(2-benzyloxy-3-methoxyphenyl)-1H-benzimidazole, 2XP, and (R) and (S)-1-(2-benzyloxy-3-methoxyphenyl)-2, 2, 2-trichloroethyl benzenesulfonate, 3BS and compared their activity to 2-benzyloxy-3-methoxybenzaldehyde, (Bn1), the parent compound. 2XP and 3BS induces cell death of U937 leukemic cell line through DNA fragmentation that lead to the intrinsic caspase 9 activation. DNA binding study primarily by the equilibrium binding titration assay followed by the Viscosity study reveal the DNA binding through groove region with intrinsic binding constant 7.39 µM/bp and 6.86 µM/bp for 3BS and 2XP respectively. 2XP and 3BS showed strong DNA binding activity by the UV titration method with the computational drug modeling showed that both 2XP and 3BS failed to form any electrostatic linkages except via hydrophobic interaction through the minor groove region of the nucleic acid. The benzylvanillin alone (Bn1) has weak anticancer activity even after it was combined with the benzimidazole (2MP), but after addition of another benzylvanillin structure (2XP), stronger activity was observed. Also, the combination of benzylvanillin with benzenesulfonate (3BS) significantly improved the anticancer activity of Bn1. The present study provides a new insight of benzyl vanillin derivatives as potential anti-leukemic agent.
A series of novel carbonyl compounds was synthesized by a simple, eco-friendly and efficient method. These compounds were screened for anti-oxidant activity, in vitro cytotoxicity and for inhibitory activity for acetylcholinesterase and butyrylcholinesterase. The effect of these compounds against amyloid β-induced cytotoxicity was also investigated. Among them, compound 14 exhibited strong free radical scavenging activity (18.39 μM) while six compounds (1, 3, 4, 13, 14, and 19) were found to be the most protective against Aβ-induced neuronal cell death in PC12 cells. Compounds 4 and 14, containing N-methyl-4-piperidone linker, showed high acetylcholinesterase inhibitory activity as compared to reference drug donepezil. Molecular docking and QSAR (Quantitative Structure-Activity Relationship) studies were also carried out to determine the structural features that are responsible for the acetylcholinesterase and butyrylcholinesterase inhibitory activity.
A series of ninety-seven diarylpentanoid derivatives were synthesized and evaluated for their anti-inflammatory activity through NO suppression assay using interferone gamma (IFN-γ)/lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages. Twelve compounds (9, 25, 28, 43, 63, 64, 81, 83, 84, 86, 88 and 97) exhibited greater or similar NO inhibitory activity in comparison with curcumin (14.7 ± 0.2 µM), notably compounds 88 and 97, which demonstrated the most significant NO suppression activity with IC50 values of 4.9 ± 0.3 µM and 9.6 ± 0.5 µM, respectively. A structure-activity relationship (SAR) study revealed that the presence of a hydroxyl group in both aromatic rings is critical for bioactivity of these molecules. With the exception of the polyphenolic derivatives, low electron density in ring-A and high electron density in ring-B are important for enhancing NO inhibition. Meanwhile, pharmacophore mapping showed that hydroxyl substituents at both meta- and para-positions of ring-B could be the marker for highly active diarylpentanoid derivatives.
Hispidin oligomers are styrylpyrone pigments isolated from the medicinal fungi Inonotus xeranticus and Phellinus linteus. They exhibit diverse biological activities and strong free radical scavenging activity. To rationalize the antioxidant activity of a series of four hispidin oligomers and determine the favored mechanism involved in free radical scavenging, DFT calculations were carried out at the B3P86/6-31+G (d, p) level of theory in gas and solvent. The results showed that bond dissociation enthalpies of OH groups of hispidin oligomers (ArOH) and spin density delocalization of related radicals (ArO•) are the appropriate parameters to clarify the differences between the observed antioxidant activities for the four oligomers. The effect of the number of hydroxyl groups and presence of a catechol moiety conjugated to a double bond on the antioxidant activity were determined. Thermodynamic and kinetic studies showed that the PC-ET mechanism is the main mechanism involved in free radical scavenging. The spin density distribution over phenoxyl radicals allows a better understanding of the hispidin oligomers formation.
A series of 21 compounds isolated from Curcuma zedoaria was subjected to cytotoxicity test against MCF7; Ca Ski; PC3 and HT-29 cancer cell lines; and a normal HUVEC cell line. To rationalize the structure-activity relationships of the isolated compounds; a set of electronic; steric and hydrophobic descriptors were calculated using density functional theory (DFT) method. Statistical analyses were carried out using simple and multiple linear regressions (SLR; MLR); principal component analysis (PCA); and hierarchical cluster analysis (HCA). SLR analyses showed that the cytotoxicity of the isolated compounds against a given cell line depend on certain descriptors; and the corresponding correlation coefficients (R2) vary from 0%-55%. MLR results revealed that the best models can be achieved with a limited number of specific descriptors applicable for compounds having a similar basic skeleton. Based on PCA; HCA and MLR analyses; active compounds were classified into subgroups; which was in agreement with the cell based cytotoxicity assay.
A series of densely functionalized piperidine (FP) scaffolds was synthesized following a diastereoselective one-pot multicomponent protocol under eco-friendly conditions. The FPs were evaluated in vitro for their acetylcholinesterase (AChE) inhibitory activity, and in silico studies for all the target compounds were carried out using pharmacophore mapping, molecular docking and quantitative structure-activity relationship (QSAR) analysis in order to understand the structural features required for interaction with the AChE enzyme and the key active site residues involved in the intermolecular interactions. Halogenation, nitration or 3,4-methylenedixoxy-substitution at the phenyl ring attached to the 2- and 6-positions of 1,2,5,6-tetrahydropyridine nucleus in compounds 14-17, 19, 20, 24 and 26 greatly enhanced the AChE inhibitory activity. The docking analysis demonstrated that the inhibitors are well-fitted in the active sites. The in silico studies enlighten the future course of studies in modifying the scaffolds for better therapeutic efficacy against the deadly Alzheimer's disease.
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.
Tuning the characteristics of solvents to fit industrial requirements has currently become a major interest in both academic and industrial communities, notably in the field of room temperature ionic liquids (RTILs), which are considered one of the most promising green alternatives to molecular organic solvents. In this work, several sets of imidazolium-based ionic liquids were synthesized, and their toxicities were assessed towards four human pathogens bacteria to investigate how tunability can affect this characteristic. Additionally, the toxicity of particular RTILs bearing an amino acid anion was introduced in this work. EC50 values (50% effective concentration) were established, and significant variations were observed; although all studied ILs displayed an imidazolium moiety, the toxicity values were found to vary between 0.05 mM for the most toxic to 85.57 mM for the least toxic. Linear quantitative structure activity relationship models were then developed using the charge density distribution (σ-profiles) as molecular descriptors, which can yield accuracies as high as 95%.
The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified as having the potential to develop inhibitors of hyaluronidase. A series of ursolic acid analogues were either synthesized via structure modification of ursolic acid 1 or commercially obtained. The evaluation of the inhibitory activity of these compounds on the hyaluronidase enzyme was conducted. Several structural, topological and quantum chemical descriptors for these compounds were calculated using semi empirical quantum chemical methods. A quantitative structure activity relationship study (QSAR) was performed to correlate these descriptors with the hyaluronidase inhibitory activity. The statistical characteristics provided by the best multi linear model (BML) (R² = 0.9717, R²cv = 0.9506) indicated satisfactory stability and predictive ability of the developed model. The in silico molecular docking study which was used to determine the binding interactions revealed that the ursolic acid analog 22 had a strong affinity towards human hyaluronidase.
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.
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.
Glioblastoma multiforme is a highly malignant, heterogenic, and drug resistant tumor. The blood-brain barrier (BBB), systemic cytotoxicity, and limited specificity are the main obstacles in designing brain tumor drugs. In this study a computational approach was used to design brain tumor drugs that could downregulate VEGF and IL17A in glioblastoma multiforme type four. Computational screening tools were used to evaluate potential candidates for antiangiogenic activity, target binding, BBB permeability, and ADME physicochemical properties. Additionally, in vitro cytotoxicity, migration, invasion, tube formation, apoptosis, ROS and ELISA assays were conducted for molecule 6 that was deemed most likely to succeed. The efflux ratio of membrane permeability and calculated docking scores of permeability to glycoproteins (P-gps) were used to determine the BBB permeability of the molecules. The results showed BBB permeation for molecule 6, with the predicted efficiency of 0.55kcal/mol and binding affinity of -37kj/mol corresponding to an experimental efflux ratio of 0.625 and predicted -15kj/mol of binding affinity for P-gps. Molecule 6 significantly affected the angiogenesis pathways by 2-fold downregulation of IL17A and VEGF through inactivation of active sites of HSP90 (predicted binding: -37kj/mol, predicted efficiency: 0.55kcal/mol) and p23 (predicted binding: 12kj/mol, predicted efficiency: 0.17kcal/mol) chaperon proteins. Additionally, molecule 6 activated the 17.38% relative fold of ROS level at 18.3μg/mL and upregulated the caspase which lead the potential synergistic apoptosis through the antiangiogenic activity of molecule 6 and thereby the highly efficacious anticancer upshot. The results indicate that the binding of the molecules to the therapeutic target is not essential to produce a lethal effect on cancer cells of the brain and that antiangiogenic efficiency is much more important.