Self-organizing Molecular Field Analysis Research Articles

Essential Structural Profile of Novel Adenosine Derivatives as Antiplatelet Aggregation Inhibitors Based on 3D-QSAR Analysis Using CoMFA, CoMSIA, and SOMFA

—In this study, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and the self-organizing molecular field analysis (SOMFA) were performed on a series of novel adenosine derivatives. Significant correlation coefficients (CoMFA, q2 = 0.560, r2 = 0.940, F value = 71.850, and SEE = 0.097; CoMSIA, q2 = 0.528, r2 = 0.943, F value = 29.29 and SEE = 0.108; SOMFA, r2 = 0.615, $$r_{{{\text{cv}}}}^{{\text{2}}}$$ = 0.577, F value = 60.797, and SEE = 0.226) were obtained, and the generated models were validated using test sets. By analyzing the corresponding contour maps in detail, new adenosine derivatives with potential efficacy were designed for synthesis in the future.

3D-QSAR Studies on New Benzothiazole-Based Dual Functional Inhibitors of BCR-ABL Kinase Including the T315I Mutant

Background: Chronic Myelogenous Leukemia (CML) is a kind of blood and bone marrow cancer. Tyrosine Kinase Inhibitors (TKIs) play an important role in the treatment of Chronic Myelogenous Leukemia (CML). Objective: The aims of this study are to demonstrate the different binding mode of Benzothiazole- Based analogues with T315I mutant Bcr-Abl and wild-type Bcr-Abl. Methods: In this paper, Self-Organizing Molecular Field Analysis (SOMFA), a simple Three- Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) analysis was performed on these 49 benzothiazole-based derivatives. Results: In this paper, highly predictive SOMFA models ( r2 = 0.748, rcv 2 = 0.720, F value = 124.9, and SEE = 0.761 for T315I mutant Bcr-Abl, r2 = 0.676, rcv 2 = 0.616, F value = 52.98, and SEE = 0.767 for wild-type Bcr-Abl) were obtained, and the generated models were validated using test sets. Conclusion: We obtained the best models of the training set and the statistical results for SOMFA model had a high predictive ability. The contour maps of potent compounds 9b was constructed by SOMFA method and some useful information was obtained for further designing new structures with high activity. Keywords: Tyrosine kinase inhibitors, CML, benzothiazole-based inhibitors, 3D-QSAR, SOMFA, T315I mutant.

Formylchromone derivatives as novel and selective PTP-1B inhibitors: a drug design aspect using molecular docking-based self-organizing molecular field analysis

Protein tyrosine phosphatase-1B (PTP-1B) acts as a negative regulator of the insulin signaling pathway. It is evident from the literature that formylchromone derivatives exhibit potent, selective, irreversible, and active site-directed PTP-1B inhibitory activity by forming covalent adduct with the active site Arg221. In the present study, a molecular docking-based self-organizing molecular field analysis (SOMFA: a 3D-QSAR method) was performed on a series of formylchromone derivatives as selective PTP-1B inhibitors. SOMFA-derived 3D-QSAR models were developed using steric and electrostatic maps which provide a significant insight into the key structural features and pharmacophoric necessities of formylchromone scaffold, essential for potent PTP-1B inhibitory activity. A library of compounds bearing formylchromone scaffold was further designed using information obtained from the developed 3D-QSAR model, and the IC50 values of the designed library were predicted using developed 3D-QSAR model. Among designed library, nine compounds demonstrated significant PTP-1B inhibitory activity as compared to the formylchromone data set and are proposed herewith for further development as antidiabetic agents.

Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery

Drug design is a process which is driven by technological breakthroughs implying advanced experimental and computational methods. Nowadays, the techniques or the drug design methods are of paramount importance for prediction of biological profile, identification of hits, generation of leads, and moreover to accelerate the optimization of leads into drug candidates. Quantitative structure–activity relationship (QSAR) has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. From decades to recent research, QSAR methods have been applied in the development of relationship between properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. Classical QSAR studies include ligands with their binding sites, inhibition constants, rate constants, and other biological end points, in addition molecular to properties such as lipophilicity, polarizability, electronic, and steric properties or with certain structural features. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free–Wilson approaches, which exploit the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, and ANN. This paper provides an overview of 1-6 dimension-based developed QSAR methods and their approaches. In particular, we present various dimensional QSAR approaches, such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis, Topomer CoMFA, self-organizing molecular field analysis, comparative molecule/pseudo receptor interaction analysis, comparative molecular active site analysis, and FLUFF-BALL, 4D-QSAR, and G-QSAR approaches.

3D-QSAR Studies on a Series of 2,4-Thiazolidinedione Derivatives: A Self- Organizing Molecular Field Analysis Approach to Design Novel PTP 1B Inhibitors

Diabetes mellitus is a group of metabolic disorders characterised by chronic hyperglycemia resulting either from a deficiency of insulin, or decreased ability to transduce the insulin signal, or both. Insulin resistance and β-cell dysfunction are two fundamental defects known to precede the onset of type 2 diabetes. PTP 1B is considered to function as a negative regulator of insulin signal transduction by dephosphorylating phosphotyrosine residues. 2,4-Thiazolidinediones (TZDs) have long been considered as antihyperglycemic agents which act by ameliorating insulin resistance and thereby normalizing elevated blood glucose level. A three dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a novel class of thiazolidinedione derivatives using self-organizing molecular field analysis (SOMFA) to correlate their molecular architecture with observed PTP 1B inhibitory activities. The master grid maps derived from the best model were used to display the contribution of both electrostatic and shape potential that can be mapped back onto structural features relating to the trends in inhibitory activities. The present SOMFA study indicated the indispensable molecular features which can be further explored for structural modifications of these lead molecules in order to optimize PTP 1B inhibitory activity.

Molecular dynamics-based self-organizing molecular field analysis on 3-amino-6-arylpyrazines as the ataxia telangiectasia mutated and Rad3 related (ATR) protein kinase inhibitors

The ataxia telangiectasia mutated and Rad3 related (ATR) protein kinase is one of the apical regulators in the DNA damaging response signaling pathways. Inhibition of ATR kinase may greatly potentiates the cyto-toxicity by DNA damaging agents to the tumor cells while, have minimum effect on normal cells. In this article, the impact of ligand conformation on the three-dimensional quantitative structure–activity relationship (3D-QSAR) model of a series of 3-amino-6-arylpyrazines as ATR kinase inhibitors was investigated. We employed molecular dynamics (MDs) simulations to get the dynamic active conformations (DACs) of the compounds in the ATP-binding site of ATR kinase. As a result, the model based on the DACs extracted from the first nanosecond MD simulation is superior to that using static active conformations from docking with r 2 = 0.917; $$q_{\text{LOO}}^{2}$$ = 0.880; standard error of estimate [SEE] = 0.259; F = 347.29; $$r_{\text{pred}}^{2}$$ = 0.923; SEEpred = 0.301. Our results highlight the importance of incorporating DACs of ligand using MD simulation in 3D-QSAR studies. This study may also provide useful information to rationalize the design of novel ATR kinase inhibitors.

Combined 3D-QSAR modeling and molecular docking study on multi-acting quinazoline derivatives as HER2 kinase inhibitors.

A series of new quinazoline derivatives has been recently reported as potent multi-acting histone deacetylase (HDAC), epidermal growth factor receptor (EGFR), and human epidermal growth factor receptor 2 (HER2) inhibitors. HER2 is one of the major targets for the treatment of breast cancer and other carcinomas. Three-dimensional structure-activity relationship (3D-QSAR) is a well-known technique, which is used to drug design and development. This technique is used for quantitatively predicting the interaction between a molecule and the active site of a specific target. For each 3D-QSAR study, a three-dimensional model is created from a large curve fit to find a fitting between computational descriptors and biological activity. This model could be used as a predictive tool in drug design. The best model has the highest correlation between theoretical and experimental data. Self-Organizing Molecular Field Analysis (SOMFA), a grid-based and alignment-dependent 3D-QSAR method, is employed to study the correlation between the molecular properties and HER2 inhibitory potency of the quinazoline derivatives. Before presentation of inhibitor structures to SOMFA study, conformation of inhibitors was determined by AutoDock4, HyperChem and AutoDock Vina, separately. Overall, six independent models were produced and evaluated by the statistical partial least square (PLS) analysis. Among the several generated 3D-QSARs, the best model was selected on the basis of its statistical significance and predictive potential. The model derived from the superposition of docked conformation with AutoDock Vina with reasonable cross-validated q(2) (0.767), non cross-validated r(2) (0.815) and F-test (97.22) values showed a desirable predictive capability. Analysis of SOMFA model could provide some useful information in the design of novel HER2 kinase inhibitors with better spectrum of activity.

Quantitative Structural Activity Relationship Studies for In Silico Prediction of In Vitro Cytotoxicity of NSAIDs in Colon Cancer Cell Lines

Non-steroidal anti-inflammatory drugs (NSAIDs) are evolving as the prototypical chemopreventive agents against colon cancer, an opinion nowadays reinforced by a substantial form of epidemiological, animal, and basic science data. Apart from their preventive role in the prophylaxis and management of colon cancer, NSAIDs at lower doses are widely employed in permutation with conventional anticancer drugs in order to reduce the dose related cytotoxic effects and to impart additive or synergistic therapeutic effect. In vitro cell-based cytotoxicity assays are effective techniques for hit ranking and lead optimization at the early stage of drug discovery. The present study has been focused on in silico screening and prediction of in vitro cytotoxicity (or growth inhibitory activity, GI50) of heterogeneous group of NSAIDs having flexibility in structure and cytotoxicity activity against DLD-1 and SW-480 colon cancer cell lines using 3DQSAR (3-dimensional quantitative structural activity relationship) SOMFA (self-organizing molecular field analysis) studies. Analysis of 3D-QSAR models through electrostatic and shape grids provide useful information about the steric, electrostatic potential contributions along with the combination of log P, total dipole moment and molar refractivity (MR) on GI50. The results obtained provided an insight on NSAIDs with optimum in vitro cytotoxicity activity and improved therapeutic profile. Keywords: NSAIDs, DLD-1 cells, SW-480 cells, COX-2 inhibitors, Colon cancer, sigmoidoscopy, colonoscopy, cohort, high-throughput, prostaglandin-mediated angiogenesis

Receptor and ligand-based 3D-QSAR study on a series of nonsteroidal anti-inflammatory drugs

Self-organizing molecular field analysis (SOMFA) has been used to study the correlation between the molecular properties of a series of 33 stilbene analogs and the cyclooxygenase-2 (COX-2) inhibitory activities. Thus, two different alignments and four charge-assigning methods using grid spacing of 1 A were investigated to produce eight SOMFA models. The best one derived from the superposition of docked conformation with PM3 charge showed satisfied statistical results. It is characterized by a good noncross-validated r 2 (0.806), cross-validated $$ r_{\text{cv}}^{ 2} $$ (0.799) and F test value (128.673). In addition, the resulted SOMFA model was validated by an external set of ten compounds. The statistical results, predicted correlation coefficient $$ r_{\text{Test}}^{ 2} $$ = 0.651, metric $$ r_{{m({\text{Test}})}}^{ 2} $$ = 0.514, and the squared correlation coefficient between observed and predicted values r 2 = 0.762, show a satisfied predictive ability. The analysis of SOMFA model, through electrostatic and shape grids, helped in understanding the possible structural modifications of molecules to improve the inhibitory potency.

In Silico Predicative Studies for Cytotoxic Potential of NSAIDs Using Self-Organizing Molecular Field Analysis

Antiproliferative potential of nonsteroidal anti-inflammatory drugs (NSAIDs) has generated an immense interest among the scientific fraternity to assess its broader role in the chemoprevention of colon cancer. Due to serious adverse events associated with the chemotherapy, NSAIDs have been exploited as adjuvants to synergize the cytotoxic potential of conventional chemotherapeutic agents at low-dose regimens. The present investigation has been focused on in silico model generation for in vitro cytotoxicity activity of the clinically active NSAIDs using self-organizing molecular field analysis (SOMFA) studies. A statistically validated robust model for a diverse group of NSAIDs having flexibility in structure and cytotoxicity activity was obtained using SOMFA. The statistical measures having good cross-validated correlation coefficient r (2) (cv) (.8291), noncross-validated correlation coefficient r (2) values (.8686), and high F test value (41.8722) proved significance in the generated model. Analysis of 3-dimensional quantitative structure activity relationship (3D-QSAR) models through electrostatic and shape grids provided additional valuable information regarding shape and electrostatic potential influence on in vitro cytotoxicity profile. The analysis of SOMFA results provided a better insight about the generation of molecular architecture of novel NSAIDs yet to be synthesized, with optimum in vitro cytotoxicity activity and improved therapeutic profile.

Self-organizing molecular field analysis of NSAIDs: Assessment of pharmacokinetic and physicochemical properties using 3D-QSPkR approach

Non-steroidal anti-inflammatory drug (NSAID) induced gastrointestinal toxicity has attracted greater attention over the years. The development of NSAIDs having safer therapeutic profile depends on the better understanding of their mechanisms, physicochemical and pharmacokinetic properties. The present investigation is aimed at in silico three dimensional quantitative structure–pharmacokinetic relationship (3D-QSPkR) assessment of a group of NSAIDs using self-organizing molecular field analysis (SOMFA) approach. Two different statistically validated models for dissociation constant (pKa) and volume of distribution (Vd) were obtained from SOMFA studies of 22 clinically active NSAIDs having diversity in structure. 3D-QSPkR models delivered useful information about the contribution of shape and electrostatic potential on pKa and Vd. The study illustrated the significance of structural variables in molecular architecture of NSAIDs especially etodolac for further optimization of ADMET properties with improved therapeutic profile.

Assessment of biological half life using in silico QSPkR approach: A self organizing molecular field analysis (SOMFA) on a series of antimicrobial quinolone drugs

The quinolones belong to a family of synthetic potent broad-spectrum antibiotics and particularly active against gram-negative organisms, especially Pseudomonas aeruginosa. A 3D-QSPkR approach has been used to obtain the quantitative structure pharmacokinetic relationship for a series of quinolone drugs using SOMFA. The series consisting of 28 molecules have been investigated for their pharmacokinetic performance using biological half life ( t 1/2). A statistically validated robust model for a diverse group of quinolone drugs having flexibility in structure and pharmacokinetic profile ( t 1/2) obtained using SOMFA having good cross-validated correlation coefficient r cv 2 (0.6847), non cross-validated correlation coefficient r 2 values (0.7310) and high F-test value (33.9663). Analysis of 3D-QSPkR models through electrostatic and shape grids provide useful information about the shape and electrostatic potential contributions on t 1/2. The analysis of SOMFA results provide an insight for the generation of novel molecular architecture of quinolones with optimal half life and improved biological profile.

3D-QSAR Studies on a Series of Dihydroorotate Dehydrogenase Inhibitors: Analogues of the Active Metabolite of Leflunomide

The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite. The statistical results, cross-validated rCV2 (0.664) and non cross-validated r2 (0.687), show a good predictive ability. The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme.

Self-organizing molecular field analysis on human β-secretase nonpeptide inhibitors: 5, 5-disubstituted aminohydantoins

5, 5-disubstituted aminohydantoins have been recently reported as potent and selective human β-secretase (BACE-1) inhibitors. Self-Organizing Molecular Field Analysis (SOMFA) is used to study the correlation between the molecular properties and biological activities of the 5, 5-disubstituted aminohydantoins inhibitors. Four different alignments and two charge-assigning methods were investigated. The model derived from the superposition of docked conformation with AM1 charge showed satisfied predictive ability, which has good non-cross-validated r2 (0.842), cross-validated q2 (0.792), F-test value (254.75) and satisfied predictive ability r2pred (0.721). Analysis of SOMFA model may provide some useful information in the design of BACE-1 inhibitors with better spectrum of activity.

Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

Ligand Based 3D-QSAR Study on a Series of Steroidal Human 5α-Reductase Inhibitors

Azasteroids constitute a class of modified steroids in which one or more carbon atoms in the steroid ring system have been substituted with nitrogen atom, having human 5α-reductase inhibitory activities for the management of benign prostatic hyperplasia (BPH). We have performed ligand based 3D-QSAR study using Self Organizing Molecular Field Analysis (SOMFA) on a series of 4-azasteroids including clinically used drug Finasteride. The SOMFA studies results indicate significant q2 (0.809), r2 (0.834) and F-test (70.060) values showing good predictive ability. The analysis of SOMFA grids provides an insight for generation of optimal molecular architecture for better human 5α-reductase inhibitory activity. Keywords: 4-Azasteroids, BPH, Finasteride, 3D-QSAR, 5α-Reductase, SOMFA

Self-Organizing Molecular Field Analysis on Pyridazine Analogues as Protein Tyrosine Phosphatase 1B (PTP 1B) Inhibitors

A 3D-QSAR study has been performed using Self-organizing molecular field analysis (SOMFA) on a novel class of pyridazine analogues as non-competitive and reversible inhibitors of PTP 1B. SOMFA is a novel 3D-QSAR methodology, similar to both comparative molecular field analysis (CoMFA) and molecular similarity studies. SOMFA studies have been performed to correlate chemical structures of pyridazine analogues with their observed PTP 1B inhibitory activity. The master grid obtained for the various SOMFA models indicates electrostatic and shape potential contributions. These can be mapped back onto the structural features relating to trends in activities of the molecules. On the basis of the spatial arrangement of the various shape and electrostatic potential contributions, new inhibitors of PTP 1B can be designed with improved spectrum of activity for the management of type 2 diabetes. Keywords: Insulin, NIDDM, PTP 1B, Pyridazine, 3D-QSAR, SOMFA

3D QSAR Studies on 1, 3, 4-Thiadiazole Derivatives: An Approach to Design Novel Anticonvulsants

A three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a series of 1, 3, 4-thiadiazole derivatives reported as anticonvulsant employing self-organizing molecular field analysis (SOMFA) techniques to investigate the structural requirements for the design of novel anticonvulsant. The training set composed of twenty two 1, 3, 4-thiadiazole derivatives that exhibit a potent activity in MMS test while predictive power was evaluated using a test set of 7 molecules. Physicochemical determinants of binding, such as steric and electrostatic properties, were mapped onto the molecular structures of 1, 3, 4-thiadiazole in order to interpret graphically the SOMFA results in terms of master grids showing various field contributions. The present 3D-QSAR studies yielded stable and statistically robust models indicated by the moderate cross correlation coefficients which may prove to be a guideline for design of novel anticonvulsants.

Self-organizing molecular field analysis of 2,4-thiazolidinediones: A 3D-QSAR model for the development of human PTP1B inhibitors

Thiazolidinediones (TZDs) have been recently developed to treat diabetic patients that can reverse insulin resistance by activation of gamma isoform of peroxisome proliferator-activated receptor (PPAR-γ). Self-Organizing Molecular Field Analysis (SOMFA) has been performed on TZD scaffold having h-PTP1B inhibitory activities for the development and optimization of lead. The master grids obtained from SOMFA models indicated significant electrostatic and shape potential contributions. These can be mapped back onto structural features relating to the trends in activities of the molecules. The robustness of generated SOMFA models was evidenced by various statistical measures. Overall, the present SOMFA study investigated the indispensable structural features of substituted TZDs which can be exploited for further structural modifications in order to optimize h-PTP1B inhibitors.

Self-organizing molecular field analysis on pregnane derivatives as human steroidal 5α-reductase inhibitors

Normal growth and development of human prostate is regulated by the androgens which balances cell proliferation and apoptosis. Testosterone (T) and dihydrotestosterone (DHT) are the two key androgens that stimulate most of the androgen action in prostate. Testosterone is converted to DHT by the membrane bound NADPH-dependent 5α-reductase enzyme. As a consequence of the important observation that progesterone and deoxycortisone inhibits the synthesis of DHT by competing with 4-en-3-one function of the testosterone for the 5α-reductase enzyme a number of pregnane derivatives were synthesized and have been reported as inhibitors of human 5α-reductase enzyme. Due to lack of information on the crystal structure of human 5α-reductase, ligand-based 3D-QSAR study has been performed on pregnane derivatives using self-organizing molecular field analysis (SOMFA) for rationalizing the molecular properties and human 5α-reductase inhibitory activities. The statistical results having good cross-validated rcv2 (0.881), non-cross-validated r2 (0.893) and F-test value (175.527), showed satisfied predictive ability rpred2 (0.777). Analysis of SOMFA models through electrostatic and shape grids provide useful information for the design and optimization of steroidal structure as novel human 5α-reductase inhibitors.