Self-organizing Molecular Field Analysis Models Research Articles

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.

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.

3D-QSAR Study on a Series of VEGFR-2 Kinase Inhibitors: 3-Pyrrole Substituted Indolin-2-Ones Compounds

The vascular endothelial growth factor receptor-2 kinases (VEGFR-2) are attractive targets for the development of anticancer agents. Self-organizing molecular field analysis (SOMFA) (a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method) is used to study the structure-activity correlation of 3-pyrrole substituted indolin-2-ones VEGFR-2 inhibitors. The statistical results, cross-validated <path id=x1D45F d=M393 379q-9 -16 -28 -29q-15 -10 -23 -2q-19 19 -36 19q-21 0 -52 -38q-57 -72 -82 -126l-40 -197q-23 -3 -75 -18l-7 7q49 196 74 335q7 43 -2 43q-7 0 -30 -14.5t-47 -37.5l-16 23q37 42 82 73t67 31q41 0 15 -113l-11 -50h4q41 71 85 117t77 46q29 0 45 -26 q13 -21 0 -43z /> <path id=x43 d=M614 175l29 -10q-33 -109 -57 -154q-121 -26 -184 -26q-90 0 -160.5 29t-112.5 77t-63.5 105.5t-21.5 119.5q0 157 108 253t277 96q36 0 71.5 -5t69 -13.5t36.5 -8.5q15 -102 20 -150l-29 -8q-20 79 -66.5 114t-128.5 35q-119 0 -187.5 -86t-68.5 -207 q0 -140 73.5 -227.5t188.5 -87.5q73 0 119.5 37.5t86.5 116.5z /> (0.5267) and non-cross-validated (0.5623), show a reliable predictive ability. The contributions of shape and electrostatic fields are 42.7% and 57.3%, respectively. Analysis of SOMFA models through shape and electrostatic grids provide useful information for the design and optimization of new 3-pyrrole substituted indolin-2-one based VEGFR-2 inhibitors.

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.

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.

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

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.

Sulphonamides as Inhibitors of Protein Tyrosine Phosphatase 1B: A Three-Dimensional Quantitative Structure-Activity Relationship Study Using Self-Organizing Molecular Field Analysis Approach

Protein tyrosine phosphatase 1B (PTP 1B), a cytosolic PTP involved in down-regulation of receptor tyrosine kinase activity following stimulation of the insulin or leptin receptors. Thus, PTP 1B inhibitors could potentially ameliorate insulin resistance and normalize plasma glucose and insulin levels without inducing hypoglycemia, and could therefore be a major advancement in the treatment of type 2 diabetes. A three-dimensional quantitative structure-activity relationship (3D-QSAR) study has been performed on a novel class of sulphonamides using self-organizing molecular field analysis (SOMFA) to correlate their chemical structures with their observed PTP 1B inhibitory activities. The master grid obtained for the various SOMFA models indicates electrostatic and shape potential contributions that can be mapped back onto structural features relating to the trends in inhibitory activities. On the basis of the spatial arrangement, steric and electrostatic factors should appropriately be taken into account for development of new potent inhibitors of PTP 1B for the management of type 2 diabetes.

3D-QSAR Studies on a Series of 5-Arylidine-2, 4-Thiazolidinediones as Aldose Reductase Inhibitors: A Self-Organizing Molecular Field Analysis Approach

Aldose Reductase (AR), the key enzyme of the polyol pathway catalyzes the reduction of glucose to sorbitol using nicotinamide adenine dinucleotide phosphate as an essential cofactor, has been demonstrated to play an important role in the pathogenesis of diabetic complications. Self Organizing Molecular Field Analysis (SOMFA), a novel three-dimensional quantitative structure activity relationship (3D-QSAR) method has been used in present case to study the correlation between the molecular properties and the aldose reductase inhibitory activities on a series of 5-arylidine-2, 4-thiazolidinedione. SOMFA calculations for both shape and electrostatic potentials were carried out. The master grid maps derived from the best model has been used to display the contribution of electrostatic potential and shape molecular field. The statistical results showed good cross-validated r(2)(CV), non cross-validated r(2), F-test set and significant predictive ability indicated by r(2)(pred). All analysis of SOMFA model may provide useful information in the design of new aldose reductase inhibitors with improved spectrum of activity for management of diabetic complications.

3D-QSAR studies on unsaturated 4-azasteroids as human 5α-reductase inhibitors: A self organizing molecular field analysis approach

Azasteroids have been reported as inhibitors of human 5α-reductase enzyme. These were designed by substitution of one carbon atom of steroidal A ring by heteroatom nitrogen. Due to lack of information on the crystal structure of human 5α-reductase, 3D-QSAR study has been performed on a series of unsaturated 4-azasteroids 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 r2cv (0.783), non cross-validated r2 (0.806) and F-test value (87.282), showed satisfied predictive ability. Analysis of SOMFA models through electrostatic and shape grids provide useful information for the design and optimization of new steroidal human 5α-reductase inhibitors.

Self organizing molecular field analysis on a series of human 5α-reductase inhibitors: Unsaturated 3-carboxysteroid

Steroidal 5 α-reductase is a NADPH dependent enzyme that catalyzes the irreversible conversion of 4-en-3-oxo-steroid testosterone to the corresponding 5 α-H-3-oxo-steroid dihydrotestosterone thus involved in Benign Prostatic Hyperplasia (BPH). As the crystal structure of target enzyme is not available; we have carried out ligand based designing using Self Organizing Molecular Field Analysis (SOMFA). SOMFA, a novel 3D-QSAR methodology used in present case to study the correlation between molecular properties and human 5α-reductase inhibitory activities of a series of unsaturated 3-carboxysteroid. The statistical results, good cross-validated r 2 cv (0.693) and non cross-validated r 2 (0.732), showed satisfied predictive ability. All analysis of SOMFA model may provide some useful information in the design of human steroidal 5α-reductase inhibitors with better spectrum of activity.

Improving the performance of SOMFA by use of standard multivariate methods

Self-Organizing Molecular Field Analysis (SOMFA) comes with a built-in regression methodology, the Self-Organizing Regression (SOR), instead of relying on external methods such as PLS. In this article we present a proof of the equivalence between SOR and SIMPLS with one principal component. Thus, the modest performance of SOMFA on complex datasets can be primarily attributed to the low performance of the SOMFA regression methodology. A multi-component extension of the original SOR methodology (MCSOR) is introduced, and the performances of SOR, MCSOR and SIMPLS are compared using several datasets. The results indicate that in general the performance of SOMFA models is greatly improved if SOR is replaced with a more sophisticated regression method. The results obtained for the Cramer (CBG) dataset further underline the fact that it is a very poor benchmark dataset and should not be used to evaluate the performance of QSAR techniques.