Significant 3D-QSAR Research Articles

Discovery of Novel Inhibitors Against Resistant Streptococcus pneumoniae MurF Enzyme using Phamracophore Modeling and QSAR Analysis

Main aim of the current study was to discover novel inhibitors against Streptococcus pneumoniae MurF enzyme using analogue based drug design. Pharmacophore mapping and 3D-QSAR analysis was performed on some previously synthesized and evaluated sulfonamide derivatives which are known to be potent inhibitors of penicillin resistant Streptococcus pneumoniae MurF receptor. 3D-QSAR study based on the principle of alignment of pharmacophoric features was performed on the same set of inhibitors using the PHASE module of Schrodinger suite. A five point pharmacophore, ADHRR, with one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic group and two aromatic rings yielded a statistically significant 3D-QSAR model with 0.96 as R2 value for three PLS components and good predictive correlation coefficients of Q2 = 0.8084, F = 140.6 and Pearson-R = 0.9157 for the test set with nine compounds. Thus, indicating good predictive power of the pharmacophore based 3D-QSAR model.

Machine learning approaches in designing anti-HIV nitroimidazoles: 2D/3D QSAR, kNN-MFA, docking, dynamics, PCA analysis and MMGBSA studies

In this study, newly synthesized 20 nitroimidazole derivatives were subjected to 2D and 3D Quantitative Structure-Activity Relationship (QSAR) study to investigate their anti-HIV activity against both ROD and IIIB strains. Later, proposed hypothesis was virtually proved by further in-silico studies. In statistically significant 2D-QSAR models r2 values for IIIB strains 0.9241 and for ROD strains 0.9412 with corresponding q2 values of 0.7706 and 0.8299, were obtained, respectively. Different models were constructed using three different kNN-MFA 3D QSAR approaches such as SW-FB, SA score, and GA. By using the generated hypothesis, newer analogues of nitroimidazole derivatives was designed and molecular modelling studies were conducted to prove the hypothesis. The three molecules were displayed the good docking scores compared to the reference molecule. The stabilities of docked complexes were analyzed by MD simulations and MMGB/SA calculations. These results offer insightful design guidance for novel anti-HIV compounds synthesis and suggest interesting directions for future pharmaceutical research.

In-silico Studies of Heterocyclic Benzoxazole Derivatives as an Anticancer Agent: Molecular Docking, 2D and 3D QSAR

In-silico molecular docking studies and QSAR study of benzoxazole derivatives synthesized by Kakkar et al. was done. Comparative studies of docking of 5-flurouracil and 20 compounds revealed considerable interactions, indicating the affinity of newly synthesized compounds for thymidylate synthase. The statistically significant 2D-QSAR models were developed using a molecular design suite (VLifeMDS 4.6). The study was performed with 20 compounds (data set) using sphere exclusion (SE) algorithm, random selection and manual selection methods used for the division of the data set into training and test set. Multiple linear regression [MLR] methodology with stepwise (SW) forward-backward variable selection method was used for building the QSAR models. The results of the 2D-QSAR models were further compared with 3D-QSAR models generated by k-Nearest Neighbor Molecular Field Analysis (kNN-MFA), investigating the substitutional requirements for the favorable anticancer activity against HCT 116 cell line and providing useful information in the characterization and differentiation of their binding sites. The results may be useful for further designing benzoxazole derivatives as anticancer agents prior to synthesis.

Finding structural requirements of structurally diverse α-glucosidase and α-amylase inhibitors through validated and predictive 2D-QSAR and 3D-QSAR analyses

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemic state. The α-glucosidase and α-amylase are considered two major targets for the management of Type 2 DM due to their ability of metabolizing carbohydrates into simpler sugars. In the current study, cheminformatics analyses were performed to develop validated and predictive models with a dataset of 187 α-glucosidase and α-amylase dual inhibitors. Separate linear, interpretable and statistically robust 2D-QSAR models were constructed with datasets containing the activities of α-glucosidase and α-amylase inhibitors with an aim to explain the crucial structural and physicochemical attributes responsible for higher activity towards these targets. Consequently, some descriptors of the models pointed out the importance of specific structural moieties responsible for the higher activities for these targets and on the other hand, properties such as ionization potential and mass of the compounds as well as number of hydrogen bond donors in molecules were found to be crucial in determining the binding potentials of the dataset compounds. Statistically significant 3D-QSAR models were developed with both α-glucosidase and α-amylase inhibition datapoints to estimate the importance of 3D electrostatic and steric fields for improved potentials towards these two targets. Molecular docking performed with selected compounds with homology model of α-glucosidase and X-ray crystal structure of α-amylase largely supported the interpretations obtained from the cheminformatic analyses. The current investigation should serve as important guidelines for the design of future α-glucosidase and α-amylase inhibitors. Besides, the current investigation is entirely performed by using non-commercial open-access tools to ensure easy accessibility and reproducibility of the investigation which may help researchers throughout the world to work more on drug design and discovery.

In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design.

Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor-ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility.

Synthesis, biological evaluation and molecular modelling of 3-Formyl-6-isopropylchromone derived thiosemicarbazones as α-glucosidase inhibitors

Type-2 Diabetes Mellitus (T2DM) is one of the most common metabolic disorders in the world and over the past three decades its incidence has increased drastically. α-Glucosidase inhibitors are used to control the hyperglycemic affect of T2DM. Herein, we report the synthesis, α-glucosidase inhibition, structure activity relationship, pharmacokinetics and docking analysis of various novel chromone based thiosemicarbazones 3(a-r). The derivatives displayed potent activity against α-glucosidase with IC50 in range of 0.11 ± 0.01–79.37 ± 0.71 µM. Among all the synthesized compounds, 3a (IC50 = 0.17 ± 0.026 µM), 3 g (IC50 = 0.11 ± 0.01 µM), 3n (IC50 = 0.55 ± 0.02 µM), and 3p (IC50 = 0.43 ± 0.025 µM) displayed higher inhibitory activity as compared to the standard, acarbose. Moreover, we have developed a statistically significant 2D-QSAR model (R2tr:0.9693; F: 50.4647 and Q2LOO:0.9190), which can be used in future to further design potent thiosemicarbazones as inhibitors of α-glucosidase.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors

The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R2 = 0.974, Q2 = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R2 pred = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA. Communicated by Ramaswamy H. Sarma

An In silico Study of Imidazo[1,2-a]pyridine Derivatives with Homology Modelled F1F0 ATP Synthase Against Mycobacterium Tuberculosis

Aim: To ascertain the binding manner and drug-likeliness profile of imidazo[1,2-a]pyridine derivatives as antitubercular agents on ATP synthase protein. Background: : Field-based 3D-QSAR, Homology modelling, Molecular Docking and ADME-T studies have been carried out to determine the binding mode and drug likeliness profile of imidazo[1,2-a]pyridine derivatives as anti-tubercular agents. Objective: To design new anti-tubercular agents using Field- based 3D-QSAR and molecular docking approach. Methods: A statistically significant 3D-QSAR model was generated with the dataset of 30 active agonists on ATP synthase whose pIC50 values range from 4.0 µM to 8.30 µM. The same dataset was analysed for ADME-T properties and docked to the homology modeled ATP synthase protein. Moreover, information from 3D-QSAR contour maps was used in designing of new molecules. Results: The constructed 3D-QSAR model had a high correlation coefficient (R2=0.9688) and cross-validation coefficient (Q2=0.9045) and F value (176) at 3 component PLS factor. The homology modeled protein ‘ac9’ was validated with various parameters like Ramachandran plot (92.5 %), ERRAT plot (98.43 %), and ProSA (-1.78 chain ‘C’; -2.74 chain ‘A’). The protein was also examined for physico-chemical properties which showed the acidic and hydrophobic nature of protein. The docking score of dataset compound no. PF19 (-9.97 Kcal/mol) was found to be almost similar with that of Bedaquiline (-10.08 Kcal/mol). Based on previous results from 3D-QSAR and Docking, four new molecules was designed. The newly designed molecules (M1-M4) were docked amongst them M3 (-9.82 Kcal/mol) scored the highest. They were further analysed for drug-likeliness, ADME-T and synthetic assesbility. The findings suggested that these compounds had a strong possibility to become ATP-synthase inhibitors. Conclusion: The various Insilico approaches used in the present study offer new avenues for designing novel molecules against ATP synthase from M. tuberculosis and can be employed for the drug discovery programme.

Combined Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Indolyl-aryl-sulfone Derivatives as New HIV1 Inhibitors.

The present study deals with the in silico of 45 indolyl-aryl-sulfones known as anti-HIV1. The data were collected from recent previously reported inhibitors and divided into a sub-set of 33 compounds as the training set and the remaining 12 compounds were kept in the test set. The selected pharmacophore-ADRRR-yielded a statistically significant 3D-QSAR model containing high confidence scores (R2 = 0.930, Q2 = 0.848, and RMSE = 0.460). The predictive power of the established pharmacophore model was validated with an external test (r2 = 0.848). A systematic virtual screening workflow shows an enrichment factor and has revealed a high predictive power. Then the model was used to screen the filtered PubChem database mapping all chemical features of model pharmacophore. The recognized hits were further assessed by in silico ADMET studies. Molecular dynamics also used to explore the stability of obtained complexes. Finally, these selected compounds are probably to become a good lead molecule for the development of effective anti-HIV-1 drugs.

In silico Prediction of Pyrazoline Derivatives as Antimalarial agents

Malaria is one of the toughest health and development challenges faced by tropical countries. The resistance of malarial parasite to available drugs and currently used chemotherapy made its emergence for development of new drugs. Pyrazoline derivatives have shown good antimalarial activity. In present work, our objective is to explore pyrazoline derivatives with in silico methods for their antimalarial activity. A five-point pharmacophore was developed using 80 molecules having logIC50 ranging from 10.39 to 6.72. The pharmacophore yielded a statistically significant 3D-QSAR model with a high correlation coefficient R2= 0.806772, cross validation coefficient Q2= 0.7154 at four component PLS factor. To evaluate the effectiveness of docking protocol, we have selected crystallographic bound compound to validate our docking procedure. Protein selected for our studies with PDB id is 2BMA having resolution 2.7 Å. Further similar orientations were observed between the superpositions of 80 compounds after pharmacophore and 3D-QSAR poses, pharmacophore and XP docking poses, 3D-QSAR and XP docking poses. These present studies will provide insight in designing novel molecules with better antimalarial activity. Results explained that two aromatic rings and two hydrophobic groups are important for the antimalarial activity. The docking studies of all selected inhibitors in the active site of 2BMA showed crucial hydrogen bond interactions with HIS95, SER97, GLN323, ARG93, ALA321, ALA346, ILE166, ILE102 and PRO96 amino acid residues.

Development of 2D and 3D Quantitative Structure Activity Relationship Models of Thiazole Derivatives for Antimicrobial Activity

A series of 20 molecules of Aryl Thiazole derivatives reported in literature Khan M S et al (2009) were used for development of 2D and 3D quantitative structure activity relationship (QSAR) models. The data set of 20 molecules were divided into training and test set in the ratio of 70:30, The biological activity was converted to logarithmic scale (pIC50) in mathematical operation mode of the software. The statistically significant 2D-QSAR models for G+ inhibition activity are r2 =0.9521 and q2 = 0.8619 and 3D QSAR results for internal (q2 = 0.8283,) and external (predictive r2 = 0.4868,) validation criteria. Thus, 3D QSAR models showed that electrostatic effects at the area of [E_685 (-10.00-0.10.00)+E_460(-0.4530-0.273)+E_531(-0.6212-0.2506)]dominantly determine the binding affinities. 2D QSAR studies revealed that T_C_C_4 descriptors were major contributing descriptor in case of G+ inhibition activity. 3D QSAR Methods were performed using kNN-MFA method. The results derived may be useful in further designing novel more potent agents.

Simulation studies, 3D QSAR and molecular docking on a point mutation of protein kinase B with flavonoids targeting ovarian Cancer

BackgroundOvarian cancer is the world’s dreaded disease and its prevalence is expanding globally. The study of integrated molecular networks is crucial for the basic mechanism of cancer cells and their progression. During the present investigation, we have examined different flavonoids that target protein kinases B (AKT1) protein which exerts their anticancer efficiency intriguing the role in cross-talk cell signalling, by metabolic processes through in-silico approaches.MethodMolecular dynamics simulation (MDS) was performed to analyze and evaluate the stability of the complexes under physiological conditions and the results were congruent with molecular docking. This investigation revealed the effect of a point mutation (W80R), considered based on their frequency of occurrence, with AKT1 protein.ResultsThe ligand with high docking scores and favourable behaviour on dynamic simulations are proposed as potential W80R inhibitors. A virtual screening analysis was performed with 12,000 flavonoids satisfying Lipinski’s rule of five according to which drug-likeness is predicted based on its pharmacological and biological properties to be active and taken orally. The pharmacokinetic ADME (adsorption, digestion, metabolism, and excretion) studies featured drug-likeness. Subsequently, a statistically significant 3D-QSAR model of high correlation coefficient (R2) with 0.822 and cross-validation coefficient (Q2) with 0.6132 at 4 component PLS (partial least square) were used to verify the accuracy of the models. Taxifolin holds good interactions with the binding domain of W80R, highest Glide score of − 9.63 kcal/mol with OH of GLU234 and H bond ASP274 and LEU156 amino acid residues and one pi-cation interaction and one hydrophobic bond with LYS276.ConclusionNatural compounds have always been a richest source of active compounds with a wide variety of structures, therefore, these compounds showed a special inspiration for medical chemists. The present study has aimed molecular docking and molecular dynamics simulation studies on taxifolin targeting W80R mutant protein of protein kinase B/serine- threonine kinase/AKT1 (EC:2.7.11.1) protein of ovarian cancer for designing therapeutic intervention. The expected result supported the molecular cause in a mutant form which resulted in a gain of ovarian cancer. Here we discussed validations computationally and yet experimental evaluation or in vivo studies are endorsed for further study. Several of these compounds should become the next marvels for early detection of ovarian cancer.

Physicochemical, Interaction & Topological Descriptors vs. hMAO-A Inhibition of Aplysinopsin Analogs: A Boulevard to the Discovery of Semi-synthetic Antidepression Agents.

Depression, a neurological disorder, is globally the 4th leading cause of chronic disabilities in human beings. This study aimed to model a 2D-QSAR equation that can facilitate the researchers to design better aplysinopsin analogs with potent hMAO-A inhibition. Aplysinopsin analogs dataset were subjected to ADME assessment for drug-likeness suitability using StarDrop software before modeled equation. 2D-QSAR equations were generated using VLife MDS 4.6. Dataset was segregated into training and test set using different methodologies, followed by variable selection. Model development was done using principal component regression, partial least square regression, and multiple regression. The dataset has successfully qualified the drug-likeness criteria in ADME simulation, with more than 90% of molecules cleared the ideal conditions, including intrinsic solubility, hydrophobicity, CYP3A4 2C9pKi, hERG pIC50, etc. 112 models were developed using multiparametric consideration of methodologies. The best six models were discussed with their extent of significance and prediction capabilities. ALP97 was emerged out as the most significant model out of all, with ~83% of the variance in the training set, the internal predictive ability of ~74%, while having the external predictive capability of ~79%. ADME assessment suggested that aplysinopsin analogs are worth investigating. Interaction among the descriptors in the way of summation or multiplication products are quite influential and yield significant 2D-QSAR models with good prediction efficiency. This model can be used to design a more potent hMAO-A inhibitor with an aplysinopsin scaffold, which can then contribute to the treatment of depression and other neurological disorders.

Discovery of New Pyrazolopyridine, Furopyridine, and Pyridine Derivatives as CDK2 Inhibitors: Design, Synthesis, Docking Studies, and Anti-Proliferative Activity.

New pyridine, pyrazoloyridine, and furopyridine derivatives substituted with naphthyl and thienyl moieties were designed and synthesized starting from 6-(naphthalen-2-yl)-2-oxo-4-(thiophen-2-yl)-1,2-dihydropyridine-3-carbonitrile (1). The chloro, methoxy, cholroacetoxy, imidazolyl, azide, and arylamino derivatives were prepared to obtain the pyridine-−C2 functionalized derivatives. The derived pyrazolpyridine-N-glycosides were synthesized via heterocyclization of the C2-thioxopyridine derivative followed by glycosylation using glucose and galactose. The furopyridine derivative 14 and the tricyclic pyrido[3′,2′:4,5]furo[3,2-d]pyrimidine 15 were prepared via heterocyclization of the ester derivative followed by a reaction with formamide. The newly synthesized compounds were evaluated for their ability to in vitro inhibit the CDK2 enzyme. In addition, the cytotoxicity of the compounds was tested against four different human cancer cell lines (HCT-116, MCF-7, HepG2, and A549). The CDK2/cyclin A2 enzyme inhibitory results revealed that pyridone 1, 2-chloro-6-(naphthalen-2-yl)-4-(thiophen-2-yl)nicotinonitrile (4), 6-(naphthalen-2-yl)-4-(thiophen-2-yl)-1H-pyrazolo[3,4-b]pyridin-3-amine (8), S-(3-cyano-6-(naphthaen-2-yl)-4-(thiophen-2-yl)pyridin-2-yl) 2-chloroethanethioate (11), and ethyl 3-amino-6-(naphthalen-2-yl)-4-(thiophen-2-yl)furo[2,3-b]pyridine-2-carboxylate (14) are among the most active inhibitors with IC50 values of 0.57, 0.24, 0.65, 0.50, and 0.93 µM, respectively, compared to roscovitine (IC50 0.394 μM). Most compounds showed significant inhibition on different human cancer cell lines (HCT-116, MCF-7, HepG2, and A549) with IC50 ranges of 31.3–49.0, 19.3–55.5, 22.7–44.8, and 36.8–70.7 μM, respectively compared to doxorubicin (IC50 40.0, 64.8, 24.7 and 58.1 µM, respectively). Furthermore, a molecular docking study suggests that most of the target compounds have a similar binding mode as a reference compound in the active site of the CDK2 enzyme. The structural requirements controlling the CDK2 inhibitory activity were determined through the generation of a statistically significant 2D-QSAR model.

Pharmacophore Generation, Quantitative Structure-Activity Relationship (QSAR), and Molecular Dynamic Simulation of Newly Substituted N-(6- Chloro-3-cyano-4-phenyl-4H-chromen-2-yl)-2-(4-chloro-phenoxy)-acetamide for Anticancer Activity

The main objective of the study was to develop the Quantitative Structure- Activity Relationship (QSAR) and pharmacophore model by using data obtained from HT-29 cells to develop potent lead molecule for the scientific community. Common pharmacophore model, atom-based 3D-QSAR, and molecular dynamic (MD) simulation were carried out via computational techniques by using 4H-chromene derivatives. The reliable common pharmacophoric hypothesis, DHH13 was generated and 3.95 survival value was also found. Furthermore, the statistically significant 3D-QSAR model was developed where r2=0.52 was found by using the Partial least squares (PLS) regression method. Phase predicted activity and Log GI50 demonstrated an important atomic position in the structure of ligands to ascertain anti colon cancer activity. Also, MD simulation was carried out between top rank leads targeting IL-6 that provided better binding conformational and complex stability into the active pocket site of the target throughout the MD simulation. The outcome of this design shows that the pharmacophore model and 3D-QSAR might be helpful for researchers in the field of medicinal chemistry to design and develop potential anti colon cancer compounds.

Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors

Pharmacophore modeling, molecular docking, and in silico ADME studies have been carried out to determine the binding mode and drug likeliness profile of Pyrrolidine derivatives as Dipeptidyl peptidase IV inhibitors. A five point pharmacophore model (AAADH_1) was generated using 96 compounds with IC50 values ranging from 1.8 to 13000 nM. A statistically significant 3D-QSAR model was generated from the pharmacophore hypothesis. The model had a high correlation coefficient (R2 - 0.92), cross validation coefficient (Q2 - 0.776) and F value (F - 144) at 6 component Partial least square factor. Pearson r of 0.7124 indicated greater degree of confidence on the model. The accuracy and predictability of the generated model were checked by Enrichment factor, Receiver operating characteristic curves, area under curve, Boltzmann-enhanced discrimination of Receiver operating characteristic and the Robust initial enhancement. To identify novel and potent Dipeptidyl peptidase IV inhibitors, virtual screening was performed using the ligand and database screening. Considering the potent hit molecules on the basis of pharmacophore virtual screening, we have designed new molecules and further subjected to see the interaction with protein. The catalytic domain of Dipeptidyl peptidase IV enzyme in complex with Vildagliptin (PDB Code: 6B1E) was obtained from protein data bank with resolution 1.77 A°. Compound 75 showed the better binding (dock score -7.966) with protein than standard drug vildagliptin (Dock Score -6.554). The hits obtained on virtual screening of the database have provided new chemical starting points for design and development of novel Dipeptidyl peptidase IV inhibitory agents. Communicated by Ramaswamy H. Sarma.

3D-QSAR Studies on 4,5-Dihydro-1H-pyrazolo [4,3-h] Quinazolines as Plk-1, CDK2/A and Aur-A Serine/Threonine Kinase Inhibitors

Background: The family of serine/threonine protein kinases is associated with peculiar tumor cell-cycle checkpoints which are overexpressed in proliferating tissues as well as in cancers, making them as potential targets for cancer chemotherapy. In the present paper, 3D-QSAR studies were carried out on 4,5-dihydro-1H-pyrazolo[4,3-h]quinazolines against serine/threonine protein kinases viz. polo-like 1 (Plk-1), cyclin dependent 2/A (CDK2/A) and Aurora-A (Aur-A) and their in vitro anti-proliferative activity on A2780 ovarian cancer cell line. Methods: 3D-QSAR models were derived using stepwise forward-backward partial least square (SWFB_PLS) regression method using VlifeMDS QSAR plus software and the docking calculations were carried out using Docking Server. Results: The derived statistically significant and predictive 3D-QSAR models exhibited correlation coefficient r2 in the range of 0.875 to 0.966 and predictive r2 in the range of 0.492 to 0.618. The hydrogen bond donor NH group joining the phenyl ring with quinazoline and terminal amide group were found to favored for Plk-1, CDK2/A and anti-proliferative activity. Estimated energy of binding of compound 45 with enzymes was in the range of -8.52 to -9.03. Conclusion: The results of 3D-QSAR studies may be useful in the development of new pyrazolo[ 4,3-h]quinazoline derivatives with better inhibitory activities against serine/threonine kinases.

In silico studies on smoothened human receptor and its antagonists in search of anticancer effects

Lately, the cancers related with abnormal hedgehog (Hh) signalling pathway are targeted by smoothened (SMO) receptor inhibitors that are rapidly developing. Still, the problems of known inhibitors such as severe side effects, weak potency against solid tumors or even the acquired resistance need to be overcome by developing new suitable inhibitors. To explore the structural requirements of antagonists needed for SMO receptor inhibition, pharmacophore mapping, 3D-QSAR models, database screening and docking studies were performed. The best selected pharmacophore hypothesis based on which statistically significant atom-based 3D-QSAR model was developed (R2 = = 0.856, Q2 = 0.611 and Pearson-R = 0.817), was further subjected to dataset screening in order to evaluate its ability to prioritize active compounds over decoys. The efficiency of one four-points pharmacophore hypothesis (AAHR.524) was observed based on good evaluation metrics such as the area under the curve (0.795), and weighted average precision (0.835), suggesting that the model is trustworthy in predicting novel inhibitors against SMO receptor.

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.

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.