Two-dimensional Quantitative Structure-activity Relationship Models Research Articles

Predictive Quantitative Structure-Activity Relationship Modeling of the Antifungal and Antibiotic Properties of Triazolothiadiazine Compounds.

Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with R2 values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds.

Quantitative structure-activity relationship and molecular docking studies on human proteasome inhibitors for anticancer activity targeting NF-κB signaling pathway

The abnormal ubiquitin-proteasome is found as an important target in various human diseases, especially in cancer, and recently it has received prevalent attention as a challenging therapeutic target. The current work is designed to derive a predictive two-dimensional quantitative structure-activity relationship model for anticancer human proteasome target of NF-κB signaling pathway. The established 2 D-QSAR is dependent on multiple linear regression approach and validated through leave-One-Out and external test set prediction method. The robust QSAR model showed the r2 of 0.83 and q2 of 0.80 and pred_r2 of 0.77. Three chemical properties, electronegativity count, average potential, and T_2_N_6 were identified as significant descriptors to predict the anticancer activities of the proteasome antagonists. Besides, the predicted top hit compounds were considered to check out the compliance with Rule of five and pharmacokinetic parameters for oral bioavailability in the human body. The molecular docking was accomplished to unravel the molecular mode of action of best-predicted compounds which was compatible with the standard drug. Following this approach, lastly two compounds NP and AP were recognized as the best candidates since these top compounds follow all the standard limit point of entire filters and indicated effective and decent docking score. The outcomes of the study sturdily suggested that the developed model and top hit compound’s binding conformation are rational in the exploration of unknown antagonist’s anticancer activity. The research would be of great support and is supposed to be of immense significance in the development and designing of drug-like candidates in preliminary drug discovery. Communicated by Ramaswamy H. Sarma

Combining molecular docking and QSAR studies for modeling the anti-tyrosinase activity of aromatic heterocycle thiosemicarbazone analogues

A collection of thirty-six aromatic heterocycle thiosemicarbazone analogues presented a broad span of anti-tyrosinase activities were designed and obtained. A robust and reliable two-dimensional quantitative structure–activity relationship model, as evidenced by the high q2 and r2 values (0.848 and 0.893, respectively), was gained based on the analogues to predict the quantitative chemical-biological relationship and the new modifier direction. Inhibitory activities of the compounds were found to greatly depend on molecular shape and orbital energy. Substituents brought out large ovality and high highest-occupied molecular orbital energy values helped to improve the activity of these analogues. The molecular docking results provided visual evidence for QSAR analysis and inhibition mechanism. Based on these, two novel tyrosinase inhibitors O04 and O05 with predicted IC50 of 0.5384 and 0.8752 nM were designed and suggested for further research.

Probing the structural requirements for thyroid hormone receptor inhibitory activity of sulfonylnitrophenylthiazoles (SNPTs) using 2D-QSAR and 3D-QSAR approaches

A series of sulfonylnitrophenylthiazoles derivatives were identified as effective targeting agents that block the interaction of the thyroid hormone receptor with its coactivators. In this work, in order to analyze the structure-activity relationship of these inhibitors and investigate the structural requirements for thyroid hormone receptor inhibitory activity, new statistically validated in silico models adopting different molecular descriptors were established. The two-dimensional quantitative structure-activity relationship models were developed using multiple linear regression method, which show both significant statistical quality and predictive ability (R 2 = 0.939, Q 2 = 0.622 for thyroid hormone receptor β; R 2 = 0.862, Q 2 = 0.763 for thyroid hormone receptor α), and different molecular descriptors were included, namely R2e, H5U, EEigo4r and Ram for thyroid hormone receptor β, MATS1P, IC2 and R5e+ for thyroid hormone receptor α. The optimum comparative molecular field analysis models were established using the template ligand-based alignment, which show satisfactory linear correlations (thyroid hormone receptor β: R 2 cv = 0.577, R 2 pred = 0.8013; thyroid hormone receptor α: R 2 cv = 0.549, R 2 pred = 0.8639). In addition, the R 2 cv of 0.543, R 2 pred of 0.8523 for thyroid hormone receptor β and R 2 cv of 0.560, R 2 pred of 0.8695 for thyroid hormone receptor α have been observed when comparative molecular similarity analysis fields were applied. All the developed statistical models give satisfactory results with accurate fitting and strong predictive abilities. Moreover, the contour maps provide an intuitive understanding of the structural requirements for the inhibitors. In conclusion, these data can provide some meaningful theoretical references to understand the factors influencing the inhibitory activity and direct the molecular design of novel inhibitors with increased activity.

Molecular modeling studies of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives as phosphatase SerB653 inhibitors

To understand the quantitative structure–activity relationships properties of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives, and to design inhibitors of phosphatase SerB653 were developed. The statistical parameters of two-dimensional quantitative structure–activity relationship model showed it has good reliability and predictive ability (q2 = 0.7180, F test = 62.046, pred_r2 = 0.7519). The best two-dimensional model suggests a chlorine atom substitution at position X4 and Y1 for enhance activity. Three-dimensional quantitative structure–activity relationship was carried out using k-nearest neighbor method and showed cross-validated correlation coefficient (q2) of 0.7484, and a predicted r2 for the external test (pred_r2) of 0.6895 were obtained with best three-dimensional quantitative structure–activity relationship model. The influences of steric and electrostatic field effects generated by the contribution plot are analyzed. Pharmacophore approach for SerB653 inhibitor consists of hydrogen bond acceptor, hydrogen bond donor, and aromatic region. Two-dimensional quantitative structure–activity relationship and three-dimensional quantitative structure–activity relationship, pharmacophore analyses of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives can provide more useful information and important structural insights for designing potent phosphatase SerB653 inhibitors.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-raycrystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed goodperformance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods.

Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem software and molecular descriptors were obtained using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares and stepwise regression, where exhibited that the volume, shape and polarity of the FTIs are important for their activities. The two-dimensional QSAR models (2D-QSAR) were obtained using both linear methods (multiple linear regression) and non-linear methods (artificial neural networks and support vector machines). The proposed QSAR models were validated using internal validation method. The results showed that the proposed 2D-QSAR models were valid and they can be used for prediction of the activities of the 2,5-diaminobenzophenone-containing FTIs. In conclusion, the 2D-QSAR models (both linear and non-linear) showed good prediction capability and the non-linear models were exhibited more accuracy than the linear models.

2D, 3D-QSAR and molecular docking of 4(1H)-quinolones analogues with antimalarial activities

Cytochrome bc1 has become a major focus as a molecular target in malaria parasites, which are the most important vector-borne infectious disease in the world. The inhibition of cytochrome bc1 blocks the mitochondrial respiratory chain and the consequent arrest of pyrimidine biosynthesis, which is essential for parasite development. The authors developed a theoretical study of two-dimensional, three-dimensional quantitative structure–activity relationships and a docking analysis of a series of 4(1H)-quinolones acting as cytochrome bc1 inhibitors. The predictive ability of the quantitative structure–activity relationship models was assessed using internal (leave-one-out cross-validation) and external (test set with 8 compounds) validation. From the two-dimensional quantitative structure–activity relationship models, the authors emphasized the following descriptors: GCUT_SLOGP_0, SLogP_VSA_5, Kier molecular flexibility index, electrophilicity index, the partition coefficient and the charge of atom 5 of the quinolone ring as the most important to explain the antimalarial activity of the compounds studied. Three-dimensional quantitative structure–activity relationship models showed that the substituents R1 and R4 in 4(1H)-quinolones analogues are key modulators to enhance the antimalarial activity. The appropriate binding conformations and orientations of these compounds interacting with cytochrome bc1 were also revealed by molecular docking. Based on the established models, 8 new compounds with highly predicted antimalarial activity have been theoretically designed and presented as a reference for synthesis and antimalarial evaluation.

Quantitative structure-activity relationship analysis of thiazolidineones: potent antidiabetic compounds

Type 2 diabetes is the most common form of diabetes, accounting for over 90% of cases. Current treatment approaches for type 2 diabetes include diet, exercise, and a variety of pharmacologic agents, including insulin, biguanides, sulfonylureas, and thiazolidinediones. In the present scenario, researchers focused themselves on thiazolidine ring-based compounds to cure type 2 diabetes mellitus. Among the peroxisome proliferator activated receptor (PPAR) family, PPAR-γ is the most effective in curing glucose homeostasis. Thiazolidine ring-based compounds act as PPAR-γ agonists, and herein, we have successfully developed nine derivatives of thiazolidine ring-based compounds that are found to be biologically potent using two-dimensional quantitative structure-activity relationship model.

In silico insights: chemical and structural characteristics associated with uridine diphosphate-glucuronosyltransferase substrate selectivity.

1. Undesirable absorption, distribution, metabolism, excretion properties are the cause of many drug development failures and this has led to the need to identify such problems earlier in the development process. This work highlights computational (in silico) approaches used to identify characteristics influencing the metabolism of uridine diphosphate (UDP)-glucuronosyltransferase (UGT) substrates. Uridine diphosphate-glucuronosyltransferase facilitates conjugation between glucuronic acid and a nucleophilic site within a substrate and is one of the major drug-metabolizing enzymes. 2. An understanding of the relevant structural and chemical characteristics of the ligand and the enzyme active site will lead to greater utilization of metabolically relevant structural information in drug design. However, an X-ray crystal structure of UGT is not yet available, little has been reported about important structurally or catalytically relevant amino acids and only recently has the reported substrate profile of UGT isoforms reached an interpretable level. 3. A database of all the known substrates and non-substrates for each human UGT isoform was assembled and a range of modelling approaches assessed. Currently, pharmacophore models developed using Catalyst (Accelrys, San Diego, CA, USA) indicate that substrates of the UGT1A family share two key hydrophobic regions 3 and 6-7 A from the site of glucuronidation in a well-defined spatial geometry. Furthermore, two-dimensional quantitative structure-activity relationship models show significant reliance on substrate lipophilicity and a range of other descriptors that are known to capture information relevant to ligand-protein interactions. 4. In conclusion, substrate-based modelling of UGT appears both useful and feasible, with significant potential for determining aspects of chemical structure associated with metabolism and to quantify the nature of the relationship for UGT substrates. The development of a novel, user-defined 'glucuronidation feature' for alignment was crucial to the development of pharmacophore-based UGT models.