3D Quantitative Structure-activity Relationship Research Articles

Benchmarking Small-Dataset Structure-Activity-Relationship Models for Prediction of Wnt Signaling Inhibition

Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size <; 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.

Identifying the Structural Features of Diphenyl Ether Analogues for InhA Inhibition: A 2D and 3D QSAR Based Study

Background: Enoyl acyl carrier protein reductase (InhA) is a validated target for Mycobacterium. It is an enzyme which is associated with the biosynthesis of mycolic acids in type II fatty acid synthase system. Mycobacterial cell wall majorly comprises mycolic acids, which are responsible for virulence of the microorganism. Several diphenyl ether derivatives have been known to be direct inhibitors of InhA. Objective: In the present work, a Quantitative Structure Activity Relationship (QSAR) study was performed to identify the structural features of diphenyl ether analogues which contribute to InhA inhibitory activity in a favourable way. Method: Both 2D and 3D QSAR models were built and compared. Several fingerprint based 2D QSAR models were generated and their relationship with the structural features was studied. Models which corroborated the inhibitory activity of the molecules with their structural features were selected and studied in detail. Results: A 2D-QSAR model, with dendritic fingerprints having regression coefficient, for test set molecules Q2 =0.8132 and for the training set molecules, R2 =0.9607 was obtained. Additionally, an atom-based 3D-QSAR model with Q2 =0.7697 and R2 =0.9159 was also constructed. Conclusion: The data reported by various models provides guidance for the designing of structurally new diphenyl ether inhibitors with potential activity against InhA of M. tuberculosis.

Design, synthesis, biological evaluation and molecular modeling study of new thieno[2,3-d]pyrimidines with anti-proliferative activity on pancreatic cancer cell lines.

Pancreatic cancer is one of the most challenging diseases with seven months only as median survival time due to its poor prognosis. Several enzymes are blamed for the progress of pancreatic cancer especially, platelet-derived growth factor receptors (PDGFRs), this in turn makes them promising targets for its treatment. In this study, twenty eight new compounds based on thieno[2,3-d]pyrimidine scaffold were synthesized as anti-pancreatic cancer agents mimicking the benzofuro[3,2-d]pyrimidine derivative, amuvatinib. Various linkers including amides, esters, ketones, urea and thiourea derivatives were utilized to study their effect on the anti-proliferative activity of these compounds. Most of the tested compounds revealed good cytotoxic activities against pancreatic carcinoma cell line PANC-1. Compound 9d showed the highest cytotoxicity with an IC50 value of 5.4µM. Furthermore, 9d showed excellent platelet derived growth factor receptor (PDGFR-α) inhibitory activity, with IC50 value 0.155µM. Docking study was carried out into PDGFR-α active site which showed comparable binding mode to that of FDA approved PDGFR-α inhibitor, imatinib. 3D-Quantitative structure activity relationship (QSAR) model was built up with five-featured pharmacophore which could be implemented for emerging effective lead structures. These compounds could serve as a new chemotype for discovering new agents for pancreatic cancer therapy.

Investigating Structural Requirements of Some Pyrimidine-linked Benzimidazole Derivatives as Anticancer Agents Against MCF-7 Cancerous Cell Line Through the use of 2D and 3D QSARs

Background: Cancer is an extremely fast, unrestrained and pathological propagation of cells. Yet there is no cancer treatment that is 100% efficient against scattered cancer. Heterocycles have been considered as a boon to treat several cancers of which pyrimidine is a core nucleus and holds an important place in cancer chemotherapy which is reflected in the use of drugs such as 5-fluorouracil, erlotinib, gefitinib and caneratinib. Also, many good antitumor active agents possess benzimidazoleas its core nucleus. Objective: To design novel pyrimidine-linked benzimidazoles and to explore their structural requirements related to anticancer potential. Methods: 2D and 3D Quantitative structure–activity relationship (QSAR) studies were carried out on a series of already synthesized 27 pyrimidine-benzimidazole derivatives. Results: Statistically significant and optimum 2D-QSAR model was developed by using step-wise variable multiple linear regression method, yielding correlation coefficient r2 = 0.89, cross-validated squared correlation coefficient q2 = 0.79 and external predictive ability of pred_r2 = 0.73 Best 3D-QSAR model was developed by employing molecular field analysis using step-wise variable k-nearest neighbor method which showed good correlative and predictive abilities in terms of q2 =0.77 and pred_r2= 0.93. Conclusion: These 2D and 3D models were found to give dependable indications which helped to optimize the pyrimidine-benzimidazole derivatives of the data set. The data yielded by 2D- QSAR and 3D-QSAR models will aid in giving better perceptions about structural requirements for developing newer anticancer agents.

Identification of potential inhibitors for HCV NS5b of genotype 4a by combining dynamic simulation, protein–ligand interaction fingerprint, 3D pharmacophore, docking and 3D QSAR

HCV NS5B polymerase has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting Hepatitis C Virus genotype 1 (HCV GT1). Hepatitis C virus genotype 4a (HCV GT4a) dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS5B polymerase of GT4a using homology modeling, protein–ligand interaction fingerprint (PLIF), docking, pharmacophore, and 3D CoMFA quantitative structure activity relationship (QSAR). Firstly, a high-quality 3D model of HCV NS5B polymerase of GT4a was constructed using crystal structure of HCV NS5B polymerase of GT1 (PDB ID: 3hkw) as a template. Then, both the model and the template were simulated to compare conformational stability. PLIF was generated using five crystal structures of HCV NS5B (PDB ID: 4mia, 4mib, 4mk9, 4mka, and 4mkb), which revealed the most important residues and their interactions with the co-crystalized ligands. After that, a 3D pharmacophore model was developed from the generated PLIF data and then used as a screening filter for 17000328 drug-like zinc database compounds. 900 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. Finally, a 3D CoMFA QSAR was developed using 42 compounds as a training and 19 compounds as a test set. The 3D CoMFA QSAR was used to design and screen some potential inhibitors, these compounds were further evaluated by the docking stage. The highest ranked five hits from docking result (compounds (p1–p4) and compound q1) were selected for further analysis.Communicated by Ramaswamy H. Sarma

In Silico Design of Novel Tetra-Substituted Pyridinylimidazoles Derivatives as c-Jun N-Terminal Kinase-3 Inhibitors, Using 2D/3D-QSAR Studies, Molecular Docking and ADMET Prediction

The c-Jun N-terminal Kinase 3 (JNK3) is a family of protein kinases that plays an important role in the neurodegenerative diseases such as Alzheimer’s disease. Here, we have performed 2D and 3D quantitative structure–activity relationship (2D/3D-QSAR), molecular docking, absorption, distribution, metabolism, excretion and toxicity (ADMET) approaches on a series of tetra-substituted pyrdinylimidazoles derivatives as JNK3 inhibitors. The aim was to design the new potent JNK3 inhibitors with high inhibitory activity. The significant 2D-QSAR models showed a good correlation between observed activity and predicted ones (R2PLS = 0.87 and R2ANN = 0.88) using partial least squares (PLS) and artificial neural network (ANN) methods. The three descriptors vsurf_G, lip-don and SMR_VSA1 that are used in the construction of 2D-QSAR models play a significant role in JNK3 inhibition. Comparative molecular similarity indices analysis (CoMSIA) was used to construct the best 3D-QSAR model using PLS method, showing good correlative and predictive capabilities (R2 = 0.96, Q2 = 0.67 and SEE = 0.14). The steric and H-bond acceptor fields play an important role in the variation of biological activity with four principal components. Molecular docking of selective inhibitors confirms the results obtained by 2D/3D-QSAR and indicate that Met 146 and Met 149 are the main residues that used to stabilize the ligand in the active site of JNK3 protein. Based on those satisfactory results, three new compounds were designed and analyzed by in silico ADMET method.

In-Silico QSAR Modelling of Predicted Rho Kinase Inhibitors Against Cardio Vascular Diseases.

Rho-kinase is an essential downstream target of GTP-binding protein RhoA, and plays a crucial role in the calcium-sensitization pathway. Rho-kinase pathway is critically involved in phosphorylation state of myosin light chain, leading to increased contraction of smooth muscles. Inhibition of this pathway has turned out to be a promising target for several indications such as cardiovascular diseases, glaucoma and inflammatory diseases. The present work focuses on a division-based 2D quantitative structure-activity relationship (QSAR) analysis along with a docking study to predict structural features that may be essential for the enhancement of selectivity and potency of the target compounds. Furthermore, a set of indoles and azaindoles were also projected based on the regression equation as novel developments. Molecular docking was applied for exploring the binding sites of the newly predicted set of compounds with the receptor. Results of the docked conformations suggested that introduction of non-bulky and substituted groups in the hinge region of ROCK-II ATP binding pocket would improve the activity by decreasing the bulkiness or length of the compounds. ADME studies were performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.

2D- and 3D-QSAR Modeling of Imidazole-Based Glutaminyl Cyclase Inhibitors.

Glutaminyl Cyclase (QC) is a novel target in the battle against Alzheimer's disease, a highly prevalent neurodegenerative disorder. QC inhibitors have the potential to be developed as therapeutically useful anti-Alzheimer's disease agents. Linear and non-linear 2D-Quantitative Structure-Activity Relationship (QSAR) models were developed using Stepwise Multiple Linear Regression (S-MLR) and neural networks. Partial least squares (PLS) method was used to develop a 3D-QSAR model. Also, the developed models were used in virtual screening of the ZINC database to identify potential QC inhibitors. The 2D neural network model showed superior predictive ability, as demonstrated by the validation parameters R2 = 0.933, Q2 = 0.886 and R2 pred = 0.911. The 3D-QSAR model's steric and electrostatic fields' isocontour maps were visualized and revealed important structural requirements associated with good activity. The virtual screening identified six compounds as potentially active QC inhibitors with improved pharmacokinetic profiles. The developed QSAR models can be used to predict the activity of compounds not yet synthesized and prioritized for their synthesis and biological evaluation. Also, potentially active QC inhibitors have been identified with attractive lead-like properties that can be used to develop anti- Alzheimer's disease agents.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships.

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

Diarylthiazole and diarylimidazole selective COX-1 inhibitor analysis through pharmacophore modeling, virtual screening, and DFT-based approaches

The current work is focused on in silico modeling of COX-1 inhibitors with enhanced safety gastric profile. A 5-point pharmacophore model, atom-based 3D quantitative structure-activity relationship (3D-QSAR) and electronic properties were computed for a series of COX-1 inhibitors. The best pharmacophore model AAHRR.10 consisting of two hydrogen bond acceptors, one hydrophobic site, and two rings was developed to derive a predictive, statistically significant 3D-QSAR model at three partial least square factors (R2 = 0.991, SD = 0.059, F = 278.5, Q2 = 0.682, RMSE = 0.325, Pearson’s R = 0.903, Spearman’s rho = 0.872). The AAHRR.10 hypothesis was validated by enrichment studies employing a custom-made validation dataset adopting selective COX-1 inhibitors extracted from ChEMBL and decoys generated via DUD methodology. The global reactivity descriptors, such as HOMO and LUMO energies, the HOMO-LUMO gaps, global hardness, softness, Fukui indices, and electrostatic potential, were carried out using density functional theory (DFT) to confirm the key structural features required to achieve COX-1 selectivity. Well-validated AAHRR.10 hypothesis was further used as 3D query in virtual screening of the DrugBank database to detect novel potential COX-1 inhibitors. Docking algorithm was applied to enhance the pharmacophore prediction and to recommend drugs for repositioning, which can interact selectively with COX-1.

Fuzzy Comprehensive Evaluation Assistant 3D-QSAR of Environmentally Friendly FQs to Reduce ADRs.

Most studies on adverse drug reactions (ADRs) of fluoroquinolones (FQs) have focused on the mechanisms of single ADRs, and no quantitative structure–activity relationship (QSAR) method studies have been carried out that combine several ADRs of FQs. In this study, an improved three-dimensional (3D) QSAR method was established using fuzzy comprehensive evaluation. This method could simultaneously consider three common ADRs of FQs using molecular parameters. The improved method could comprehensively predict three ADRs of FQs and provide direction for the development of new drugs with lower ADRs than the originals. According to the improved method, 48 derivatives with lower ADRs (decreased by 4.86% to 50.92%) were designed from pazufloxacin. Three derivatives with a higher genotoxicity, higher photodegradation, and lower bioconcentration than pazufloxacin were selected using the constructed QSAR methods of the FQs. Finally, three traditional 3D-QSAR methods of single ADR were constructed to validate the improved method. The improved method was reasonable, with a relative error range of 0.96% to 4.30%. This study provides valuable reference data and will be useful for the development of strategies to produce new drugs with few ADRs. In the absence of complementary biological studies of these adverse drug reactions, the results reported here may be quite divergent from those found in humans or experimental animals in vivo. One major reason for this is that many adverse drug reactions are dependent upon enzyme-catalyzed metabolic activation (toxication) or on non-enzymatic conversion to toxic products and are not due to the parent drug moiety.

Fragment- and negative image-based screening of phosphodiesterase 10A inhibitors.

A novel virtual screening methodology called fragment- and negative image-based (F-NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A-specific small-molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F-NiB combines features from both fragment-based drug discovery and negative image-based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein-bound ligand(s) are seamlessly combined with the negative image of the target's ligand-binding cavity. This cavity- and fragment-based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F-NiB methodology, 3D quantitative structure-activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67μM range in a radiometric assay. In a larger context, the study shows that the F-NiB provides a flexible way to incorporate small-molecule fragments into the drug discovery.

QSAR and Molecular Modeling Studies on a Series of Pyrrolidine Analogs Acting as BACE-1 Inhibitors

Background: β-Site amyloidal precursor protein (APP) cleavage enzyme (BACE-1) is reported as prime cause for progession of Alzheimer’s disease (AD). It is a form of dementia characterized by degeneration of neurones in brain. Therefore, attempts have been made to find potent inhibitors of this enzyme. Methods: The paper presents an division-based 2D quantitative structure-activity relationship (QSAR) study on a series of BACE-1 inhibitors to analyse the structural features that may be important to increase the potency of the compounds. Results: The study led to predict some potential leads for the development of potent inhibitors of BACE-1. One of the molecule with pyrrolidine and pyrrolidinone substitutions exhibited drugreceptor interactions comparable with reference drug. Conclusion: The hydrogen-bond interactions between the molecules and the receptor basically control the BACE-1 inhibition activity of the compounds.

Development of sodium glucose co-transporter 2 (SGLT2) inhibitors with novel structure by molecular docking and dynamics simulation

In this study, molecular docking studies were carried out to explore the binding interactions of sodium glucose co-transporter 2 (SGLT2) with its inhibitors. A correlation between the docking scores and the experimental bioactivity was observed (R2 = 0.8368, N = 24). The new inhibitors were designed using the 3D quantitative structure activity relationship (3D-QSAR) method, and the activities were predicted by the docking method. In order to understand the structure-activity correlation of compound 1m (the highest score of docking) and compound 1t (the lowest score), we carried out a combined molecular dynamics simulation and MM-GBSA method. It was found that, in the system of SGLT2-1m, the interaction between Gln271 and Val272 exhibited significant effects, which were absent in the SGLT2-1t system. This study is expected to shed light on the mechanism of action of compound 1m, leading to development of active drug candidates targeting SGLT2.

Quantitative Structure-Activity Relationship Analysis of Selective Rho Kinase Inhibitors as Neuro-regenerator Agents.

To understand the role of Rho (serine/threonine) kinases in the treatment of neurological segments, attempts have been made to find potent inhibitors of Rho enzyme by a 2D quantitative structure-activity relationship (QSAR) model. QSAR studies were executed on urea-based scaffolds from aniline and benzylamine analogues, which were aligned for generation of a chemometric-based model. Multivariate statistical approaches were applied including linear and nonlinear analysis such as multiple linear regression, partial least square and artificial neural network for the generation of model, and also an application of (in silico) absorption, distribution, metabolism, excretion studies was performed to ascertain the novelty and drug-like properties of the intended molecules. Ligand based analysis was implemented and showed excellent statistical relevance such as S value=0.38, F value=48.41, r=0.95, r²=0.91, and r²cv=0.86. Five illuminating variables, i.e., vesicle-associated membrane protein (VAMP) polarization YY component (whole molecule), VAMP dipole Y component (whole molecule), VAMP dipole Z component (whole molecule), Kier ChiV6 path index (whole molecule), and moment of inertia 2 size (whole molecule), were found and they have a profound influence on the potency of the compounds. The values of standard statistical parameters reveal the predictive power and robustness of this model and also provide valuable insight into the significance of five descriptors. The acquired physicochemical properties (electronic, topological, and steric) show the important structural features required for activity against Rho kinase. After performing Lipinski's rule of five on urea-based derivatives no molecule was violating the rule. Therefore, these features can be effectively employed for the modeling and screening of active neurological agents as novel Rho kinase inhibitors.

In Silico Identification of Potential Inhibitor Against a Fungal Histone Deacetylase, RPD3 from Magnaporthe Oryzae

Histone acetylation and deacetylation play an essential role in the epigenetic regulation of gene expression. Histone deacetylases (HDAC) are a group of zinc-binding metalloenzymes that catalyze the removal of acetyl moieties from lysine residues from histone tails. These enzymes are well known for their wide spread biological effects in eukaryotes. In rice blast fungus, Magnaporthe oryzae, MoRPD3 (an ortholog of Saccharomyces cerevisiae Rpd3) was shown to be required for growth and development. Thus in this study, the class I HDAC, MoRpd3 is considered as a potential drug target, and its 3D structure was modelled and validated. Based on the model, a total of 1880 compounds were virtually screened (molecular docking) against MoRpd3 and the activities of the compounds were assessed by docking scores. The in silico screening suggested that [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid (−8.7 kcal/mol) and [4-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid (−8.5 kcal/mol) are effective in comparison to trichostatin A (−7.9 kcal/mol), a well-known general HDAC inhibitor. The in vitro studies for inhibition of appressorium formation by [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid has resulted in the maximum inhibition at lower concentrations (1 μM), while the trichostatin A exhibited similar levels of inhibition at 1.5 μM. These findings thus suggest that 3D quantitative structure activity relationship studies on [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid compound can further guide the design of more potential and specific HDAC inhibitors.

Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D-QSAR Analysis.

In the present work, 2D- and 3D-quantitative structure-activity relationship (QSAR) analysis has been employed for a diverse set of eighty-nine quinoxalinones to identify the pharmacophoric features with significant correlation with the aldose reductase inhibitory activity. Using genetic algorithm (GA) as a variable selection method, multivariate linear regression (MLR) models were derived using a pool of molecular descriptors. All the six-descriptor based GA-MLR QSAR models are statistically robust with coefficient of determination (R2 )>0.80 and cross-validated R2 >0.77. The derived GA-MLR models were thoroughly validated using internal and external and Y-scrambling techniques. The CoMFA like model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, is highly robust with R2 >0.93 and cross-validated R2 >0.73. The established QSAR and CoMFA like models are proficient in identify key pharmacophoric features that govern the aldose reductase inhibitory activity of quinoxalinones.

QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies.

A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition; it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interest for such research due to their high variability and the necessity of their monitoring to provide safety for agricultural products and foodstuffs. We evaluated the binding of 20 triazines with polyclonal (pAb) and monoclonal (mAb) antibodies obtained using atrazine as the immunogenic hapten. A total of over 3000 descriptors were used in the quantitative structure-activity relationship (QSAR) analysis of binding activities (pIC50). A comparison of the two enzyme immunoassay systems showed that the system with pAb is much easier to describe using 2D QSAR methodology, while the system with mAb can be described using the 3D QSAR CoMFA. Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q2 (‘leave-many-out’) is equal to 0.498, and for monoclonal antibodies, q2 is equal to 0.566. Obviously, in the case of pAb, we deal with several targets, while in the case of mAb the target is one, and therefore it is easier to describe it using specific fields of molecular interactions distributed in space.

Anti-tumor effects and 3D-quantitative structure-activity relationship analysis of bufadienolides from toad venom

Toad venom (venenum bufonis, also called Chan'su) has been widely used for centuries in China to treat different diseases, especially for cancer. Bufadienolides are mainly responsible for the anti-cancer effects of toad venom. However, systematic chemical composition and cytotoxicity as well as key pharmacophores of these bufadienolides from toad venom have not yet been defined clearly. To enrich the understanding of the diversity of bufadienolides and to find bufadienolides with better activities from toad venom. This study was carried out to isolate chemical constituents, research their anti-tumor effects and mechanisms by MTT assay, flow cytometry and Western blotting, and develop a CoMFA and CoMSIA quantitative structure-activity relationship (QSAR) model for illustrating the vital relationship between the chemical structures and cytotoxicities. Among 47 natural bufadienolides, most of bufadienolides (21 compounds isolated in this study and 26 compounds isolated previously) could significantly inhibit the proliferation of cancer cells, and compounds 1, 8, 12, 18 and 19 showed the most potent inhibitory activity against four types of human tumor cells. Compound 18 induced G2/M cell cycle arrest and apoptosis. Moreover, 3D contour maps generated from CoMFA and CoMSIA identified several pharmacophores of bufadienolides responsible for the anti-tumor activities. Our study might provide reliable information for future structure modification and rational drug design of bufadienolides with anticancer activities in medical chemistry.

3D QSAR AND DOCKING STUDY OF INDOLE DERIVATIVES AS SELECTIVE COX-2 INHIBITORS

Objective: Non-steroidal anti-inflammatory agents (NSAIDs) continue to be one of the most widely used groups of therapeutic agents. QSAR (quantitative structure-activity relationship) approach is a very useful and widespread technique for drug design. 3D QSAR facilitates evaluation of three-dimensional molecular fields around molecules and generates a relationship of these fields' values with the activity.&#x0D; Methods: 3D QSAR study was performed on selected twenty-four compounds from synthesized indole derivatives using the stepwise variable selection k-nearest neighbor (kNN) molecular field analysis approach for indicating the contribution of the steric and electronic field for activity. The docking study was performed to further confirm the binding affinity of synthesized molecules (ligands) to COX-2 enzyme as well as to study binding nature.&#x0D; Results: Statistically significant model was generated using VLife Molecular Design Suite 3.5 software with cross-validated correlation coefficient q2 of 0.9461 and high predictive correlation coefficient (Pred_r2) of 0.8782 indicating that the model is robust. The results of docking study suggest that the synthesized compounds have a comparable binding affinity with the COX-2 enzyme.&#x0D; Conclusion: The present study may prove to be helpful in the development and optimization of existing indole derivatives as anti-inflammatory agents with selective COX-2 inhibition.