Molecular insights of benzodipyrazole as CDK2 inhibitors: combined molecular docking,molecular dynamics,and 3D QSAR studies

Abstract

Benzodipyrazoles have been previously evaluated for their in vitro CDK2 inhibitory activity. In the current investigation, we identified a six-feature common pharmacophore model (AADDRR.33) which is predicted to be responsible for CDK2 inhibition. An efficient 3D QSAR (r^2?=?0.98 and q^2?=?0.82) model was also constructed by employing PLS regression analysis. From the molecular docking studies, we examined the binding patterns of compound 7aa with the target protein and also calculated the binding energy using MM-GBSA calculations. Three hydrogen bonds with Lys 33, Glu 81, and Leu 83 are conserved even after 1000?ps run in a molecular dynamics simulation. We identified the slight displacement in bond lengths and the conformational changes occurred during the dynamics. The results also elucidated the protein residue–ligand interaction fractions which clearly explained the involvement of non-H-bond interactions.     

Exploration of potential RSK2 inhibitors by pharmacophore modelling,structure-based 3D-QSAR,molecular docking study and molecular dynamics simulation

Abstract

The p90 ribosomal s6 kinase 2 (RSK2) is a promising target because of its over expression and activation in human cancer cells and tissues. Over the last few years, significant efforts have been made in order to develop RSK2 inhibitors to treat myeloma, prostatic cancer, skin cancer and etc., but with limited success so far. In this paper, pharmacophore modelling, molecular docking study and molecular dynamics (MD) simulation have been performed to explore the novel inhibitors of RSK2. Pharmacophore models were developed by 95 molecules having pIC₅₀ ranging from 4.577 to 9.000. The pharmacophore model includes one hydrogen bond acceptor (A), one hydrogen bond donor (D), one hydrophobic feature (H) and one aromatic ring (R). It is the best pharmacophore hypothesis that has the highest correlation coefficient (R² = 0.91) and cross validation coefficient (Q² = 0.71) at 5 component PLS factor. It was evaluated using enrichment analysis and the best model was used for virtual screening. The constraints used in this study were docking score, ADME properties, binding free energy estimates and IFD Score to screen the database. Ultimately, 12 hits were identified as potent and novel RSK2 inhibitors. A 15 ns molecular dynamics (MD) simulation was further employed to validate the reliability of the docking results.     

Ligand-based and structure-based approaches in identifying ideal pharmacophore against c-Jun N-terminal kinase-3

Structure and ligand based pharmacophore modeling and docking studies carried out using diversified set of c-Jun N-terminal kinase-3 (JNK3) inhibitors are presented in this paper. Ligand based pharmacophore model (LBPM) was developed for 106 inhibitors of JNK3 using a training set of 21 compounds to reveal structural and chemical features necessary for these molecules to inhibit JNK3. Hypo1 consisted of two hydrogen bond acceptors (HBA), one hydrogen bond donor (HBD), and a hydrophobic (HY) feature with a correlation coefficient (r²) of 0.950. This pharmacophore model was validated using test set containing 85 inhibitors and had a good r² of 0.846. All the molecules were docked using Glide software and interestingly, all the docked conformations showed hydrogen bond interactions with important hinge region amino acids (Gln155 and Met149) and these interactions were compared with Hypo1 features. The results of ligand based pharmacophore model (LBPM) and docking studies are validated each other. The structure based pharmacophore model (SBPM) studies have identified additional features, two hydrogen bond donors and one hydrogen bond acceptor. The combination of these methodologies is useful in designing ideal pharmacophore which provides a powerful tool for the discovery of novel and selective JNK3 inhibitors.     

Pharmacophore mapping of arylamino-substituted benzo[b]thiophenes as free radical scavengers

Predictive pharmacophore models have been developed for a series of arylamino-substituted benzo[b]thiophenes exhibiting free radical scavenging activity. 3D pharmacophore models were generated using a set of 20 training set compounds and subsequently validated by mapping 6 test set compounds using Discovery Studio 2.1 software. Further model validation was performed by randomizing the data using Fischer’s validation technique at the 95% confidence level. The most predictive pharmacophore model developed using the conformers obtained from the BEST method showed a correlation coefficient (r) of 0.942 and consisted of three features: hydrogen bond donor, hydrogen bond acceptor and aromatic ring. Acceptable values of external validation parameters, like R_pred² R_{{\rm{pred}}}^2 (0.853) and r_{m( test )}² r_{m\left( {test} \right)}^2 (0.844), also implied that the external predictivity of the model was significant. The development of further pharmacophore models using conformers obtained from the FAST method yielded a few models with good predictivity, with the best one (r = 0.904) consisting of two features: hydrogen bond donor and hydrogen bond acceptor. Significant values of external validation parameters, R_pred² R_{{\rm{pred}}}^2 (0.913) and r_{m( test )}² r_{m\left( {test} \right)}^2 (0.821), also reflect the high predictive ability of the model. Again, Fischer validation results implied that the models developed were robust enough and their good results were not based on mere chance. These validation approaches indicate the reliability of the predictive abilities of the 3D pharmacophore models developed here, which may thus be further utilized as a 3D query tool in the virtual screening of new chemical entities with potent antioxidant activities.     

Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors

Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to N-methyl pyrimidones as HIV-1 integrase inhibitors. Based on the ligand-based pharmacophore model, we got 5-point pharmacophore model AADDR, with two hydrogen bond acceptors (A), two hydrogen bond donors (D) and one aromatic ring (R). The generated pharmacophore-based alignment was used to derive a predictive atom-based 3D-QSAR model for the training set (r²?=?0.92, SD?=?0.16, F?=?84.8, N?=?40) and for test set (Q²?=?0.71, RMSE?=?0.06, Pearson R?=?0.90, N?=?10). From these results, AADDR pharmacophore feature was selected as best common pharmacophore hypothesis, and atom-based 3D-QSAR results also support the outcome by means of favourable and unfavourable regions of hydrophobic and electron-withdrawing groups for the most potent compound 30. These results can be useful for further design of new and potent HIV-1 IN inhibitors.     

Atom-based 3D-QSAR,induced fit docking,and molecular dynamics simulations study of thieno[2,3-b]pyridines negative allosteric modulators of mGluR5

Atom-based three dimensional-quantitative structure–activity relationship (3D-QSAR) model was developed on the basis of 5-point pharmacophore hypothesis (AARRR) with two hydrogen bond acceptors (A) and three aromatic rings for the derivatives of thieno[2,3-b]pyridine, which modulates the activity to inhibit the mGluR5 receptor. Generation of a highly predictive 3D-QSAR model was performed using the alignment of predicted pharmacophore hypothesis for the training set (R²?=?0.84, SD?=?0.26, F?=?45.8, N?=?29) and test set (Q²?=?0.74, RMSE?=?0.235, Pearson-R?=?0.94, N?=?9). The best pharmacophore hypothesis AARRR was selected, and developed three dimensional-quantitative structure activity relationship (3D-QSAR) model also supported the outcome of this study by means of favorable and unfavorable electron withdrawing group and hydrophobic regions of most active compound 42d and least active compound 18b. Following, induced fit docking and binding free energy calculations reveals the reliable binding orientation of the compounds. Finally, molecular dynamics simulations for 100?ns were performed to depict the protein–ligand stability. We anticipate that the resulted outcome could be supportive to discover potent negative allosteric modulators for metabotropic glutamate receptor 5 (mGluR5).     

Pharmacophore modeling, 3D-QSAR,and in silico ADME prediction of N-pyridyl and pyrimidine benzamides as potent antiepileptic agents

Biological mechanism attributing mutations in KCNQ2/Q3 results in benign familial neonatal epilepsy (BFNE), a rare form of epilepsy and thus neglected. It offers a potential target for antiepileptic drug discovery. In the present work, a pharmacophore-based 3D-QSAR model was generated for a series of N-pyridyl and pyrimidine benzamides possessing KCNQ2/Q3 opening activity. The pharmacophore model generated contains one hydrogen bond donor (D), one hydrophobic (H), and two aromatic rings (R). They are the crucial molecular write-up detailing predicted binding efficacy of high affinity and low affinity ligands for KCNQ2/Q3 opening activity. Furthermore, it has been validated by using a biological correlation between pharmacophore hypothesis-based 3D-QSAR variables and functional fingerprints of openers responsible for the receptor binding and also by docking of these benzamides into the validated homology model. Excellent statistical computational tools of QSAR model such as good correlation coefficient (R^2?>^?0.80), higher F value (F?>?39), and excellent predictive power (Q²?>?0.7) with low standard deviation (SD <0.3) strongly suggest that the developed model could be used for prediction of antiepileptic activity of newer analogs. A preliminary pharmacokinetic profile of these derivatives was also performed on the basis of QikProp predictions.     

A combination of pharmacophore modeling,atom-based 3D-QSAR,molecular docking and molecular dynamics simulation studies on PDE4 enzyme inhibitors

Phosphodiesterases 4 enzyme is an attractive target for the design of anti-inflammatory and bronchodilator agents. In the present study, pharmacophore and atom-based 3D-QSAR studies were carried out for pyrazolopyridine and quinoline derivatives using Schrödinger suite 2014-3. A four-point pharmacophore model was developed using 74 molecules having pIC₅₀ ranging from 10.1 to 4.5. The best four feature model consists of one hydrogen bond acceptor, two aromatic rings, and one hydrophobic group. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a high correlation coefficient (R^2?=?.9949), cross validation coefficient (Q^2?=?.7291), and Pearson-r (.9107) at six component partial least square factor. The external validation indicated that our QSAR model possessed high predictive power with R² value of .88. The generated model was further validated by enrichment studies using the decoy test. Molecular docking, free energy calculation, and molecular dynamics (MD) simulation studies have been performed to explore the putative binding modes of these ligands. A 10-ns MD simulation confirmed the docking results of both stability of the 1XMU–ligand complex and the presumed active conformation. Outcomes of the present study provide insight in designing novel molecules with better PDE4 inhibitory activity.     

Computational investigation of binding mechanism of substituted pyrazinones targeting corticotropin releasing factor-1 receptor deliberated for anti-depressant drug design

In spite of various research investigations towards anti-depressant drug discovery program, no one drug has not yet launched last 20 years. Corticotropin-releasing factor-1 (CRF-1) is one of the most validated targets for the development of antagonists against depression, anxiety and post-traumatic stress disorders. Various research studies suggest that pyrazinone based CRF-1 receptor antagonists were found to be highly potent and efficacious. In this research investigation, we identified the pharmacophore and binding pattern through 2D and 3D-QSAR and molecular docking respectively. Molecular dynamics studies were also performed to explore the binding pattern recognition. We establish the relationship between activity and pharmacophoric features to design new potent compounds. The best 2D-QSAR model was generated through multiple linear regression method with r² value of 0.97 and q² value of 0.89. Also 3D-QSAR model was obtained through k-nearest neighbor molecular field analysis method with q² value of 0.52 and q²_se value of 0.36. Molecular docking and binding energy were also evaluated to define binding patterns and pharmacophoric groups, including (i) hydrogen bond with residue Asp284, Glu305 and (ii) π–π stacking with residue Trp9. Compound 11i has the highest binding affinity compared to reference compounds, so this compound could be a potent drug for stress related disorders. Most of the compounds, including reference compounds were found within acceptable range of physicochemical parameters. These observations could be provided the leads for the design and optimization of novel CRF-1 receptor antagonists.

Communicated by Ramaswamy H. Sarma     

Reliable structural information for rational design of benzoxazole type potential cholesteryl ester transfer protein (CETP) inhibitors through multiple validated modeling techniques

Abstract

The drug design and discovery of lipid modulators is very demanding as no new molecule has entered into the market in the last 35 years. Cholesteryl ester transfer protein (CETP) is a promising target as lipid modulators. Inhibition of the CETP enzyme reduces the risk of cardiovascular events. The first CETP inhibitor torcetrapib and related drug candidates failed in the clinical trial due to the off-target effects leading to high toxicity. Thus, newer CETP inhibitors have now paramount importance to accelerate the drug discovery efforts in the field of cardiovascular disease (CVD). In the present study, 140 benzoxazole compounds were studied by using different chemometric techniques, for example, pharmacophore mapping, molecular docking, three-dimensional quantitative structure–activity relationship comparative molecular field analysis (3D-QSAR CoMFA), topomer CoMFA and Bayesian classification, in order to generate complete and reliable information regarding the structural requirements for the CETP inhibition. The best pharmacophore hypothesis was statistically significant (regression coefficient of 0.957 and a lower root mean square of 0.890). Molecular docking study revealed that cyano-substituted compounds form hydrogen bond with targeted macromolecule. The 3D-QSAR CoMFA model also produced a leave-one-out (LOO) cross-validated Q² of 0.527, an R² of 0.853 and an R²_Pred of 0.603. Similarly, two topomer CoMFA models were also statistically significant and reliable in terms of their Q², R² and R²_Pred values. The Bayesian classification study also provided the excellent ROC values of 0.919 and 0.939 for training and test sets, respectively. Overall, this study may help in the rational design of newer benzoxazole type compounds with higher CETP inhibition.

Communicated by Ramaswamy H. Sarma     

Pharmacophore generation,atom-based 3D-QSAR,molecular docking and molecular dynamics simulation studies on benzamide analogues as FtsZ inhibitors

FtsZ is an appealing target for the design of antimicrobial agent that can be used to defeat the multidrug-resistant bacterial pathogens. Pharmacophore modelling, molecular docking and molecular dynamics (MD) simulation studies were performed on a series of three-substituted benzamide derivatives. In the present study a five-featured pharmacophore model with one hydrogen bond acceptors, one hydrogen bond donors, one hydrophobic and two aromatic rings was developed using 97 molecules having MIC values ranging from .07 to 957 μM. A statistically significant 3D-QSAR model was obtained using this pharmacophore hypothesis with a good correlation coefficient (R² = .8319), cross validated coefficient (Q² = .6213) and a high Fisher ratio (F = 103.9) with three component PLS factor. A good correlation between experimental and predicted activity of the training (R² = .83) and test set (R² = .67) molecules were displayed by ADHRR.1682 model. The generated model was further validated by enrichment studies using the decoy test and MAE-based criteria to measure the efficiency of the model. The docking studies of all selected inhibitors in the active site of FtsZ protein showed crucial hydrogen bond interactions with Val 207, Asn 263, Leu 209, Gly 205 and Asn-299 residues. The binding free energies of these inhibitors were calculated by the molecular mechanics/generalized born surface area VSGB 2.0 method. Finally, a 15 ns MD simulation was done to confirm the stability of the 4DXD–ligand complex. On a wider scope, the prospect of present work provides insight in designing molecules with better selective FtsZ inhibitory potential.     

Molecular docking and pharmacophore model studies of Rho kinase inhibitors

Molecular docking and pharmacophore model approaches were used to characterise the binding features of four different series of Rho kinase (ROCK) inhibitors. Docking simulation of 20 inhibitors with ROCK was performed. The binding conformations and binding affinities of these inhibitors were obtained using AutoDock 4.0 software. The predicted binding affinities correlate well with the activities of these inhibitors (R ² = 0.904). 3D pharmacophore models were generated for ROCK based on highly active inhibitors implemented in Catalyst 4.11 program. The best pharmacophore model consists of one hydrogen bond acceptor feature and two hydrophobic features, and they all seemed to be essential for inhibitors in terms of their binding activities. It is anticipated that the findings reported in this paper may provide very useful information for designing new ROCK inhibitors.     

Noscapinoids with anti-cancer activity against human acute lymphoblastic leukemia cells (CEM): a three dimensional chemical space pharmacophore modeling and electronic feature analysis

We have identified a new class of microtubule-binding compounds—noscapinoids—that alter microtubule dynamics at stoichiometric concentrations without affecting tubulin polymer mass. Noscapinoids show great promise as chemotherapeutic agents for the treatment of human cancers. To investigate the structural determinants of noscapinoids responsible for anti-cancer activity, we tested 36 structurally diverse noscapinoids in human acute lymphoblastic leukemia cells (CEM). The IC₅₀ values of these noscapinoids vary from 1.2 to 56.0 μM. Pharmacophore models of anti-cancer activity were generated that identify two hydrogen bond acceptors, two aromatic rings, two hydrophobic groups, and one positively charged group as essential structural features. Additionally, an atom-based quantitative structure–activity relationship (QSAR) model was developed that gave a statistically satisfying result (R ² = 0.912, Q ² = 0.908, Pearson R = 0.951) and effectively predicts the anti-cancer activity of training and test set compounds. The pharmacophore model presented here is well supported by electronic property analysis using density functional theory at B3LYP/3-21*G level. Molecular electrostatic potential, particularly localization of negative potential near oxygen atoms of the dimethoxy isobenzofuranone ring of active compounds, matched the hydrogen bond acceptor feature of the generated pharmacophore. Our results further reveal that all active compounds have smaller lowest unoccupied molecular orbital (LUMO) energies concentrated over the dimethoxy isobenzofuranone ring, azido group, and nitro group, which is indicative of the electron acceptor capacity of the compounds. Results obtained from this study will be useful in the efficient design and development of more active noscapinoids.     

Pharmacophore mapping of a series of pyrrolopyrimidines,indolopyrimidines and their congeners as multidrug-resistance-associated protein (MRP1) modulators

Pharmacophore mapping studies were undertaken for a series of molecules belonging to pyrrolopyrimidines, indolopyrimidines and their congeners as multidrug resistance-associated protein (MRP1) modulators. A five-point pharmacophore with two hydrogen bond acceptors (A), one lipophilic/hydrophobic group (H), one positive ionic feature (P) and one aromatic ring (R) as pharmacophoric features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r ² = 0.799 for training set molecules. The model generated showed excellent predictive power, with a correlation coefficient Q ² = 0.679 for an external test set of 20 molecules. The pharmacophore was further validated using four structurally diverse compounds with MRP1 modulatory activity. These compounds mapped well onto four of the five features of the pharmacophore. The pharmacophore proposed here was then utilised for the successful retrieval of active molecules with diverse chemotypes from database search. The geometry and features of pharmacophore are expected to be useful for the design of selective MRP1 inhibitors. Figure Alignment of multidrug resistance-associated protein (MRP1) inhibitors with the developed pharmacophore. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Pharmacophore modeling,comprehensive 3D-QSAR,and binding mode analysis of TGR5 agonists

Takeda G-protein-coupled receptor 5 (TGR5) is emerging as an important and promising target for the development of anti-diabetic drugs. Pharmacophore modeling and atom-based 3D-QSAR studies were carried out on a new series of 5-phenoxy-1,3-dimethyl-1H-pyrazole-4-carboxamides as highly potent agonists of TGR5. The generated best six featured pharmacophore model AAHHRR consists of two hydrogen bond acceptors (A): two hydrophobic groups (H) and two aromatic rings (R). The constructed 3D-QSAR model acquired excellent correlation coefficient value (R^2?=^?0.9018), exhibited good predictive power (Q^2?=^?0.8494) and high Fisher ratio (F?=?61.2). The pharmacophore model was validated through Guner–Henry (GH) scoring method. The GH value of 0.5743 indicated that the AAHHRR model was statistically valuable and reliable in the identification of TGR5 agonists. Furthermore, the combined approach of molecular docking and binding free energy calculations were carried out for the 5-phenoxy-1,3-dimethyl-1H-pyrazole-4-carboxamides to explore the binding mode and interaction pattern. The generated contour maps revealed the important structural insights for the activity of the compounds. The results obtained from this study could be helpful in the development of novel and more potent agonists of TGR5.     

Virtual Screening and Drug Design for PDE-5 Receptor from Traditional Chinese Medicine Database

Abstract

Erectile dysfunction (ED) is a sexual disorder mainly caused by decrease in cellular concentration of cyclic guanosine monophosphate (cGMP), which is degraded by phosphodiesterase type-5 (PDE-5). As a potent therapeutic target, inhibitors such as Viagra®, Cialis®, and Levitra® have already been developed to target PDE-5 for treating ED; traditional Chinese medicine, Epimedium sagittatum, also has shown prominent results as well. To developed new PDE-5 inhibitors, we performed a virtual screening of traditional Chinese medicine (TCM) database and docking analyses to identify candidates. Known PDE-5 inhibitors were used to construct a three dimensional quantitative structure-activity relationship (3D QSAR) model by HypoGen program. From docking analyses, isochlorogenic acid b was identified as the most potential inhibitory compound. De novo evolution designed 47 derivatives. Of the 47 derivatives, seven were able to map into the pharmacophore model, and these seven compounds were suggested to be the most promising leads for inhibiting PDE-5. An analysis of the hydrogen bond interactions formed between the docked ligands and PDE-5 identified ASN662, SER663 and GLN817 as the most frequently interacting residues. A total of eight novel leading compounds were identified to have favorable interaction with PDE-5. These compounds all had hydrogen bond interactions with three key residues that could be further investigated for understanding of PDE-5 and ligands interaction.     

Elucidation of the mechanism of N-demethylation catalyzed by cytochrome P450 monooxygenase is facilitated by exploiting nitrogen-15 heavy isotope effects

¹⁵N heavy isotope effects are especially useful when detail is sought pertaining to the reaction mechanism for the cleavage of a C–N bond. Their potential in assisting to describe the mechanism of N-demethylation of tertiary amines by the action of cytochrome P450 monooxygenase has been investigated. As a working model for the first step, oxidation of the N-methyl group to N-methoxyl, tropine and a cytochrome P450 monooxygenase reaction centre composed of a truncated heme with sulfhydryl as the axial ligand were used. It is apparent that this first step of the reaction proceeds via a hydrogen atom transfer mechanism. Transition states for this step are described for both the high spin (⁴TS_H) and low spin (²TS_H) pathways in both gas and solvation states. Hence, overall normal secondary ¹⁵N KIE could be calculated for the reaction path modeled in the low spin state, and inverse for the reaction modeled in the high spin state. This partial reaction has been identified as the probable rate limiting step. The model for the second step, fission of the C–N bond, consisted of N-methoxylnortropine and two molecules of water. A transition state described for this step, TS_CN, gives a strongly inverse overall theoretical ¹⁵N KIE.     

BCR-ABL tyrosine kinase inhibitor pharmacophore model derived from a series of phenylaminopyrimidine-based (PAP) derivatives

To reveal novel insights into the inhibition of BCR-ABL tyrosine kinase, pharmacophore mapping studies were performed for a series of phenylaminopyrimidine-based (PAP) derivatives, including imatinib (Gleevec). A seven-point pharmacophore model with one hydrophobic group (H), two hydrogen bond donors (D) and four aromatic rings (R) was developed using phase (pharmacophore alignment & scoring engine). The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of 0.886 and a survival score of 4.97 for training set molecules. The model showed excellent predictive power, with a correlation coefficient of Q² = 0.768 for an external test set of ten molecules. The results obtained from our studies provide a valuable tool for designing new lead molecules with potent activity.     

Pharmacophore based 3D-QSAR modeling and free energy analysis of VEGFR-2 inhibitors

VEGFR-2, a transmembrane tyrosine kinase receptor is responsible for angiogenesis and has been an attractive target in treating cancers. The inhibition mechanism of structurally diverse urea derivatives, reported as VEGFR-2 inhibitors, was explored by pharmacophore modeling, QSAR, and molecular dynamics based free energy analysis.The pharmacophore hypothesis AADRR, resulted in a highly significant atom based 3D-QSAR model (r² = 0.94 and q² = 0.84). Binding free energy analysis of the docked complexes of highly active and inactive compounds, after 7 ns MD simulation, revealed the importance of van der Waals interaction in VEGFR-2 inhibition. The decomposition of binding free energy on a per residue basis disclosed that the residues in hinge region and hydrophobic pocket play a role in discriminating the active and inactive inhibitors. Thus, the present study proposes a pharmacophore hypothesis representing the identified interactions pattern and its further application as a template in screening databases to identify novel VEGFR-2 inhibitor scaffolds.     

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT₇ inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT₇ inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q² (cross validated correlation coefficient) of 0.642, 0.602 and r² (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT₇ antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r² obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.