CoMSIA Studies Research Articles

A Novel Strategy for Designing the Selective PPAR Agonist by the “Sum of Activity” Model

Peroxisome proliferator-activated receptors α, δ, and γ are a collection of ligand-activated transcription factors crucial in lipid and glucose homeostasis. The involvement of these receptors in lipid metabolism makes them perfect therapeutic target for treating obesity and stroke. In this study, ‘sum of activity’ model was employed to design multi-target agonists. We used a new strategy to design agonists that fit both α and δ but not γ to avoid side effect. The CoMFA and CoMSIA models were used to explore the pharmacophore features by constructing three individual models: (a) α-model, (b) δ-model and (c) γ-model, and two sum models: (d) α, δ- model, and (e) α, δ, γ- model. The CoMFA model yielded a significant cross validation value, q2, of 0.729 and non-cross validation value, r2, of 0.933 in the alpha;, δ-model. The CoMSIA studies yielded the best predictive models with q2 of 0.622 in A + S and with r2 of 0.911 in the α, δ-model. Finally, we proposed that distinct features shown in models (a), (b), (d) but not (c) and (e) should be accounted in designing weight-controlling drugs.

Development of predictive pharmacophore model for in silico screening, and 3D QSAR CoMFA and CoMSIA studies for lead optimization, for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-alpha converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.

A comparison of different electrostatic potentials on prediction accuracy in CoMFA and CoMSIA studies

Computational chemistry is playing an increasingly important role in drug design and discovery, structural biology, and quantitative structure–activity relationship (QSAR) studies. For QSAR work, selecting an appropriate and accurate method to assign the electrostatic potentials of each atom in a molecule is a critical first step. So far several commonly used methods are available to assign charges. However, no systematic comparison of the effects of electrostatic potentials on QSAR quality has been made. In this study, twelve semi-empirical and empirical charge-assigning methods, AM1, AM1-BCC, CFF, Del-Re, Formal, Gasteiger, Gasteiger–Hückel, Hückel, MMFF, PRODRG, Pullman, and VC2003 charges, have been compared for their performances in CoMFA and CoMSIA modeling using several standard datasets. Some charge assignment models, such as Del-Re, PRODRG, and Pullman, are limited to specific atom and bond types, and, therefore, were excluded from this study. Among the remaining nine methods, the Gasteiger–Hückel charge, though commonly used, performed poorly in prediction accuracy. The AM1-BCC method was better than most charge-assigning methods based on prediction accuracy, though it was not successful in yielding overall higher cross-validation correlation coefficient ( q 2) values than others. The CFF charge model worked the best in prediction accuracy when q 2 was used as the evaluation criterion. The results presented should help the selection of electrostatic potential models in CoMFA and CoMSIA studies.

Multiple Receptor Docking and Dock Pose Clustering as a Tool for CoMFA and CoMSIA Studies

Multiple Receptor Docking and Dock Pose Clustering as a Tool for CoMFA and CoMSIA Studies

3D QSAR Studies on Benzoxazoles and Oxazolo-(4, 5-b)pyridines as Anti-fungal agents

Benzoxazoles and Oxazolo-[4,5-b]pyridines have been reported as potent anti-fungal agents. 3D QSAR tools including CoMFA and CoMSIA have been known to be a promising approaches is to correlate structures and activity which further enable the medicinal chemists to design more potent molecules thus curtailing the cost and time in drug research. CoMFA and CoMSIA studies have been carried out on 31 molecules of benzoxazole and oxazolopyridines in order to determine the structural properties required for effective antifungal activity. 26 compounds were evaluated for establishing QSAR model, which was then validated by predicting the activities of five test set molecules. All the molecules were aligned by SYBYL database alignment which led to a best model with q2 value of 0.835, r2=0.976 and r2pred=0.773. This model was further employed to derive CoMSIA models, a best model with steric, electrostatic, hydrophobic and hydrogen bond acceptor indices exhibited q2 = 0.812, r2=0.971 and r2pred=0.81. The models thus obtained from this study can be useful for the design and development of new potential anti-fungal agents.

Application of 3D QSAR CoMFA/CoMSIA and in silico docking studies on novel renin inhibitors against cardiovascular diseases

For the first time, a set of renin inhibitors were subjected to the 3D QSAR/CoMFA and CoMSIA studies. The utility of renin inhibitors in the treatment of cardiovascular diseases has not been fully explored yet. At the moment, aliskiren is the first and only existing renin inhibitor in the drug market. The performed 3D QSAR/CoMFA and CoMSIA in combination with docking studies included aliskiren and 37 derivatives possessing a wide variety of bioactivity. The obtained results may aid in the design of novel bioactive renin inhibitors.

ChemInform Abstract: 3D QSAR/CoMFA and CoMSIA Studies on Antileukemic Steroidal Esters Coupled with Conformationally Flexible Nitrogen Mustards.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Development of CoMFA and CoMSIA models of affinity and selectivity for indole ligands of cannabinoid CB1 and CB2 receptors

This paper describes CoMFA and CoMSIA studies for affinity and selectivity of a series of indole ligands to cannabinoid CB1 and CB2 receptors. The developed models have proven to be predictive, with average q 2 of 0.675 and average r 2 of 0.855, demonstrating a good statistical validation. The obtained results have helped us to understand the structural motifs that are responsible for the affinity and selectivity of some of these derivatives towards each subtype of cannabinoid receptor and have demonstrated that the exploited 3D-QSAR methods could be useful tools for the design of new safer analogues presenting better affinity and selectivity profiles.

3D QSAR/CoMFA and CoMSIA Studies on Antileukemic Steroidal Esters Coupled with Conformationally Flexible Nitrogen Mustards

Thirty-eight antileukemic steroidal esters possessing conformationally flexible nitrogen mustards were studied, and the 3D QSAR/CoMFA and CoMSIA methodologies were applied in order to derive the correlation between their structure and the in vivo antileukemic activity. These compounds show significantly reduced toxicity and possibly increased bioavailability compared to free nitrogen mustards and therefore constitute potent antileukemic drugs. Both the CoMFA and CoMSIA studies gave similar results indicating that the steric effect and the hydrophobic/hydrophilic balance especially in the steroidal part of the molecules probably determined their bioactivity. Of paramount interest is the observation that the orientation of the alkylating part of the SMEs toward the surface of ring B of the steroidal skeleton was related with increased activity. Concerning the steroidal part, the presence of hydrophobic groups in rings B and D was found to be important for enhanced activity. Enhancement of antileukemic potency is further observed if hydrophilic/H-bond acceptor groups are present at the positions 7 and 17 of the steroidal skeleton. Leapfrog simulations provided novel compounds which lead our future synthetic endeavor for obtaining SMEs with optimum bioactivity.

3D QSAR CoMFA/CoMSIA, molecular docking and molecular dynamics studies of fullerene-based HIV-1 PR inhibitors

For the first time, a set of experimentally reported [60] fullerene derivatives were subjected to the 3D-QSAR/CoMFA and CoMSIA studies. The aim of this study is to propose a series of novel [60] fullerene-based inhibitors with optimal binding affinity for the HIV-1 PR enzyme. The position of the template molecule at the cavity of HIV-1 PR was optimized and 3D QSAR models were developed. Relative contributions of steric/electrostatic fields of the 3D-QSAR/CoMFA and CoMSIA models have shown that steric effects govern the bioactivity of the compounds, but electrostatic interactions play also an important role.The de novo drug design Leapfrog simulations provided a series of novel compounds with predicted improved inhibition effect.

Structure-based CoMFA and CoMSIA study of indolinone inhibitors of PDK1

Phosphoinositide-dependent protein kinase-1 (PDK1) is a Ser/Thr kinase which phosphorylates and activates members of the AGC kinase group known to control processes such as tumor cell growth, protection from apoptosis, and tumor angiogenesis. In this paper, CoMFA and CoMSIA studies were carried out on a training set of 56 conformationally rigid indolinone inhibitors of PDK1. Predictive 3D QSAR models, established using atom fit alignment rule based on crystallographic-bound conformation, had cross-validated (r(cv)(2)) values of 0.738 and 0.816 and non-cross-validated (r(ncv)(2)) values of 0.912 and 0.949 for CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 14 compounds, which gave predictive correlation coefficients (r(pred)(2)) of 0.865 and 0.837, respectively. Structure-based interpretation of the CoMFA and CoMSIA field properties provided further insights for the rational design of new PDK1 inhibitors.

3D QSAR studies of 1,3,4-benzotriazepine derivatives as CCK 2 receptor antagonists

A number of CCK 2 antagonists have been reported to play an important role in controlling gastric acid-related conditions, nervous system related disorders and certain types of cancer. To obtain the helpful information for designing potent antagonists with novel structures and to investigate the quantitative structure–activity relationship of a group of 62 different CCK 2 receptor antagonists with varying structures and potencies, CoMFA, CoMSIA, and HQSAR studies were carried out on a series of 1,3,4-benzotriazepine-based CCK 2 receptor antagonists. QSAR models were derived from a training set of 49 compounds. By applying leave-one-out (LOO) cross-validation study, cross-validated ( r cv 2 ) values of 0.673 and 0.608 and non-cross-validated ( r ncv 2 ) values of 0.966 and 0.969 were obtained for the CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 13 compounds, which gave predictive correlation coefficients ( r pred 2 ) of 0.793 and 0.786, respectively. HQSAR was also carried out as a complementary study, and the best HQSAR model was generated using atoms, bonds, hydrogen atoms, and chirality as fragment distinction with fragment size (2–5) and six components showing r cv 2 and r ncv 2 values of 0.744 and 0.918, respectively. CoMFA steric and electrostatic, CoMSIA hydrophobic and hydrogen bond acceptor fields, and HQSAR atomic contribution maps were used to analyze the structural features of the datasets that govern their antagonistic potency.

Insight into the structural requirements of proton pump inhibitors based on CoMFA and CoMSIA studies

In the present study, a series of 179 quinoline and quinazoline heterocyclic analogues exhibiting inhibitory activity against Gastric (H+/K+)-ATPase were investigated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods. Both the models exhibited good correlation between the calculated 3D-QSAR fields and the observed biological activity for the respective training set compounds. The most optimal CoMFA and CoMSIA models yielded significant leave-one-out cross-validation coefficient, q 2 of 0.777, 0.744 and conventional cross-validation coefficient, r 2 of 0.927, 0.914 respectively. The predictive ability of generated models was tested on a set of 52 compounds having broad range of activity. CoMFA and CoMSIA yielded predicted activities for test set compounds with r pred 2 of 0.893 and 0.917 respectively. These validation tests not only revealed the robustness of the models but also demonstrated that for our models r pred 2 based on the mean activity of test set compounds can accurately estimate external predictivity. The factors affecting activity were analyzed carefully according to standard coefficient contour maps of steric, electrostatic, hydrophobic, acceptor and donor fields derived from the CoMFA and CoMSIA. These contour plots identified several key features which explain the wide range of activities. The results obtained from models offer important structural insight into designing novel peptic-ulcer inhibitors prior to their synthesis.

3D-QSAR Studies on Cannabinoid CB1 Receptor Agonists: G-Protein Activation as Biological Data

G-protein activation via the CB1 receptor was determined for a group of various CB1 ligands and utilized as biological activity data in subsequent CoMFA and CoMSIA studies. Both manual techniques and automated docking at CB1 receptor models were used to obtain a common alignment of endocannabinoid and classical cannabinoid derivatives. In the final alignment models, the endocannabinoid headgroup occupies a unique region distinct from the classical cannabinoid structures, supporting the hypothesis that these structurally diverse molecules overlap only partially within the receptor binding site. Both CoMFA and CoMSIA produce statistically significant models based on the manual alignment and a docking alignment at one receptor conformer. Leave-half-out cross-validation and progressive scrambling were successfully used in assessing the predictivity of the QSAR models.

CoMFA, ADVANCE CoMFA AND CoMSIA STUDIES ON THE OXADIAZOLE SUBSTITUTED α-ISOPROPOXY PHENYLPROPIONIC ACIDS FOR PPARα AGONISTIC ACTIVITY

The fibrate class drugs effect on lipid metabolism through PPARα receptor. Thus, PPARα/γ dual agonists may provide superior therapy to the current PPARγ-selective agonists, due to the additional lipid control afforded by the PPARα component. In order to elucidate the structural and physicochemical requirements responsible for binding to the PPARα enzyme and to develop predictive models, CoMFA, advance CoMFA and CoMSIA studies have been carried out on a series of oxadiazole-substituted α-isopropoxy phenylpropanoic acids. The CoMFA model developed using most common structure based alignment and tripos standard field demonstrated high predictive ability (q2 = 0.573, r2 = 0.711). Further the PLS coefficient contour maps from CoMFA and CoMSIA, well explain the structural variation of the PPARα inhibitors.

CoMFA and CoMSIA studies of angiotensin (AT1) receptor antagonists

CoMFA and CoMSIA studies of angiotensin (AT1) receptor antagonists

CoMFA and CoMSIA studies of angiotensin (AT1) receptor antagonists.

Two 3D-QSAR methods--CoMFA and CoMSIA--were applied to a set of 38 angiotensin receptor (AT1) antagonists. The conformation and alignment of molecules were obtained by a novel method - consensus dynamics. The representation of biological activity, partial charge formalism, absolute orientation of the molecules in the grid, and grid spacing were also studied for their effect on the CoMFA models. The models were thoroughly validated through trials using scrambled activities and bootstrapping. The best CoMFA model had a cross-validated correlation coefficient ( q2) of 0.632, which improved with "region focusing" to 0.680. This model had a "predictive" r2 of 0.436 on a test series that was unique and with little representation in the training set. Although the "predictive" r2 of the best CoMSIA model, which included steric, electrostatic, and hydrogen bond acceptor fields was higher than that of the best CoMFA model, the other statistical parameters like q2, r2, F value, and s were unsatisfactory. The contour maps generated using the best CoMFA model were used to identify the structural features important for biological activity in these compounds.

Investigation of structural requirements of anticancer activity at the paclitaxel/tubulin binding site using CoMFA and CoMSIA

CoMFA and CoMSIA analysis were utilized in this investigation to define the important interacting regions in paclitaxel/tubulin binding site and to develop selective paclitaxel-like active compounds. The starting geometry of paclitaxel analogs was taken from the crystal structure of docetaxel. A total of 28 derivatives of paclitaxel were divided into two groups—a training set comprising of 19 compounds and a test set comprising of nine compounds. They were constructed and geometrically optimized using SYBYL v6.6. CoMFA studies provided a good predictability ( q 2=0.699, r 2=0.991, PC=6, S.E.E.=0.343 and F=185.910). They showed the steric and electrostatic properties as the major interacting forces whilst the lipophilic property contribution was a minor factor for recognition forces of the binding site. These results were in agreement with the experimental data of the binding activities of these compounds. Five fields in CoMSIA analysis (steric, electrostatic, hydrophobic, hydrogen-bond acceptor and donor properties) were considered contributors in the ligand–receptor interactions. The results obtained from the CoMSIA studies were: q 2=0.535, r 2=0.983, PC=5, S.E.E.=0.452 and F=127.884. The data obtained from both CoMFA and CoMSIA studies were interpreted with respect to the paclitaxel/tubulin binding site. This intuitively suggested where the most significant anchoring points for binding affinity are located. This information could be used for the development of new compounds having paclitaxel-like activity with new chemical entities to overcome the existing pharmaceutical barriers and the economical problem associated with the synthesis of the paclitaxel analogs. These will boost the wide use of this useful class of compounds, i.e. in brain tumors as the most of the present active compounds have poor blood–brain barrier crossing ratios and also, various tubulin isotypes has shown resistance to taxanes and other antimitotic agents.

Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices.

The program SEAL is suited to describe the electrostatic, steric, hydrophobic, and hydrogen bond donor and acceptor similarity of different molecules in a quantitative manner. Similarity scores AF can be calculated for pairs of molecules, using either a certain molecular property or a sum of weighted properties. Alternatively, their mutual similarity can be derived from distances d or covariances c between SEAL-based property fields that are calculated in a regular grid. For a set of N chemically related molecules, such values form an N x N similarity matrix which can be correlated with biological activities, using either regression analysis and an appropriate variable selection procedure or partial least-squares (PLS) analysis. For the Cramer steroid data set, the test set predictivities (r2pred = 0.53-0.84) of different PLS models, based on a weighted sum of molecular properties, are superior to published results of CoMFA and CoMSIA studies (r2pred = 0.31-0.40), regardless of whether a common alignment or individual, pairwise alignments of all molecules are used in the calculation of the similarity matrices. Training and test set selections have a significant influence on the external predictivities of the models. Although the SEAL similarity score between two molecules is a single number, its value is based on the 3D properties of both molecules. The term 3D quantitative similarity-activity analyses (3D QSiAR) is proposed for approaches which correlate 3D structure-derived similarity matrices with biological activities.