3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists

3D-QSAR studies on the derivatives of 1-(3,3-diphenylpropyl)-piperidinyl amide and urea as CCR5 receptor antagonists were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The global minimum energy conformer of the template molecule, the most active and pharmacokinetically stable molecule of the series, was obtained by systematic search and used to build structures of the molecules in the dataset. The best predictions for the CCR5-receptor were obtained with the CoMFA standard model (q ² = 0.787, r ² = 0.962) and CoMSIA model combined steric, electrostatic and hydrophobic fields (q ² = 0.809, r ² = 0.951). The predictive ability of CoMFA and CoMSIA were determined using a test set of 12 compounds giving predictive correlation coefficients of 0.855 and 0.83, respectively, indicating good predictive power. Further, the robustness of the model was verified by bootstrapping analysis. The contour maps produced by the CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series. Based on the CoMFA and CoMSIA analysis, we have identified some key features in the series that are responsible for CCR5 antagonistic activity which may be used to design more potent 1-(3,3-diphenylpropyl)-piperidinyl derivatives and predict their activity prior to synthesis. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Predicting anti-cancer activity of quinoline derivatives: CoMFA and CoMSIA approach

The 3D quantitative structure-activity relationships of 31 quinoline nuclei containing compounds and their biological activity have been investigated to establish various models. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies resulted in reliable and significant computational models. The obtained CoMFA model showed high predictive ability with q(2) = 0.592, r(2) = 0.966 and standard error of estimation (SEE) = 0.167, explaining majority of the variance in the data with two principal components. Predictions obtained with CoMSIA steric, electrostatic, hydrophobic, hydrogen-bond acceptor and donor fields (q(2) = 0.533, r(2) = 0.985) showed high prediction ability with minimum SEE (0.111) and four principal components. The information obtained from the CoMFA and CoMSIA contour maps can be utilized for the design and development of topoisomerase-II inhibitors for synthesis.     

3D QSAR studies on T-type calcium channel blockers using CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of isoxazolyl compounds as a potent T-type calcium channel blockers. A set of 24 structurally similar compounds served to establish the model. Four different conformations of the most active compound were used as template structures for the alignment, three of which were obtained from Catalyst pharmacophore modeling and one by using SYBYL random search option. All CoMFA and CoMSIA models gave cross-validated r(2) (q(2)) value of more than 0.5 and conventional r(2) value of more than 0.85. The predictive ability of the models was validated by an external test set of 10 compounds, which gave satisfactory pred r(2) values ranging from 0.577 to 0.866 for all models. Best predictions were obtained with CoMFA std model of Conformer no: 3 alignment (q(2)=0.756, r(2)=0.963), giving predictive r(2) value of 0.866 for the test set. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands accounting for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, hydrophobic and hydrogen bonding fields.     

3D–QSAR studies of orvinol analogs as κ-opioid agonists

Orvinols are potent analgesics that target opioid receptors. However, their analgesic mechanism remains unclear and no significant preference for subtype opioid receptor has been achieved. In order to find new orvinols that target the κ-receptor, comparative 3D–QSAR studies were performed on 26 orvinol analogs using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best predictions for the κ-receptor were obtained with the CoMFA standard model (q ²=0.686, r ²=0.947) and CoMSIA model combined steric, electrostatic, hydrophobic, and hydrogen bond donor/acceptor fields (q ²=0.678, r ²=0.914). The models built were further validated by a test set made up of seven compounds, leading to predictive r ² values of 0.672 for CoMFA and 0.593 for CoMSIA. The study could be helpful for designing and prepare new category κ-agonists from orvinols.      

Molecular modeling studies,synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors

In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure–activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q² and a non-cross-validation r², which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC₅₀) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

3D-QSAR studies on malonyl coenzyme A decarboxylase inhibitors

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of Malonyl Co-A decarboxylase (MCD) inhibitors (Cheng et al. J. Med. Chem.2006, 49, 1517-1525 and Cheng et al. Bioorg. Med. Chem. Lett.2006, 16, 695-700). These inhibitors have shown protective action on the ischemic heart by inhibiting fatty acid oxidation. The CoMFA model produced statistically significant results, with the cross-validated and conventional correlation coefficients being 0.544 and 0.986, respectively. The best results were obtained by combining steric, electrostatic, hydrophobic, and H-bond acceptor fields in CoMSIA, in which case the respective cross-validated and conventional correlation coefficients were 0.551 and 0.918. The predictive ability of CoMFA and CoMSIA, determined using a test set of 24 compounds, gave predictive correlation coefficients of 0.718 and 0.725, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps may be of utility in the design of more potent MCD inhibitors.     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

Models of the pharmacophoric pattern and affinity trend of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate derivatives as σ1 ligands

Sigma-1 (σ₁) affinities of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate (MAPCC) derivatives were modelled by the genetic algorithm with linear assignment of hypermolecular alignment of datasets (GALAHAD) and the comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) methods. GALAHAD was used for deriving the 3D pharmacophore pattern that encompasses the most potent σ₁ ligands within this series. Five MAPCC derivatives with a high σ₁ affinity were used for deriving this model. The obtained model included a nitrogen atom, the hydrophobes and the hydrogen bond acceptor features; it was able to identify other potent σ₁ ligands. On the other hand, CoMFA and CoMSIA methods were used for deriving quantitative structure–activity relationship (QSAR) models. All QSAR models were trained with 17 compounds, after which they were evaluated for predictive ability with additional five compounds. The best QSAR model was obtained by using CoMSIA, including steric, electrostatic and hydrophobic fields, and had a good predictive quality according to both internal and external validation criteria. In general, the models described herein provide meaningful information relevant for the rational design of new σ₁ ligands.     

Comparative molecular field analysis and comparative molecular similarity indices analysis of human thymidine kinase 1 substrates

Thymidine kinase 1 (TK1) is a key target for antiviral and anticancer chemotherapy. Three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques was applied to analyze the phosphorylation capacity of a series of 31 TK1 substrates. The optimal predictive CoMFA model with 26 molecules provided the following values: cross-validated r(2) (q(2))=0.651, non-cross-validated r(2)=0.980, standard error of estimate (s)=0.207, F=129.3. For the optimal CoMSIA model the following values were found: q(2)=0.619, r(2)=0.994, s=0.104, F=372.2. The CoMSIA model includes steric, electrostatic, and hydrogen bond donor fields. The predictive capacity of both models was successfully validated by calculating known phosphorylation rates of five TK1 substrates that were not included in the training set. Contour maps obtained from CoMFA and CoMSIA models correlated with the experimentally developed SAR.     

CoMFA and CoMSIA 3D QSAR analysis on N1-arylsulfonylindole compounds as 5-HT6 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series of N(1)-arylsulfonylindole compounds as 5-HT(6) antagonists. Evaluation of 20 compounds served to establish the models. The lowest energy conformer of compound 1 obtained from random search was used as template for alignment. The best predictions were obtained with CoMFA standard model (q2 = 0.643, r2 = 0.939 ) and with CoMSIA combined steric, electrostatic, hydrophobic, and hydrogen bond acceptor fields (q2 = 0.584, r2 = 0.902 ). Both the models were validated by an external test set of eight compounds giving satisfactory predictive r2 values of 0.604 and 0.654, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps can be used for further design of specific 5-HT(6) antagonists.     

Comparative QSAR studies on peptide deformylase inhibitors

Comparative quantitative structure–activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r ² = 0.957, q ² = 0.569; CoMSIA r ² = 0.924, q ² = 0.520; HQSAR r ² = 0.860, q ² = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r ² values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor–ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF. Figure Superimposition of comparative molecular field analysis (CoMFA) contour plot in the active site of peptide deformylase (PDF)     

3D-QSAR studies of farnesyltransferase inhibitors: a comparative molecular field analysis approach

3D-QSAR analysis has been performed on a series of previously synthesized benzonitrile derivatives, which were screened as farnesyltransferase inhibitors, using comparative molecular field analysis (CoMFA) with partial least-square fit to predict the steric and electrostatic molecular field interactions for the activity. The CoMFA study was carried out using a training set of 34 compounds. The predictive ability of the model developed was assessed using a test set of eight compounds (r(pred)(2) as high as 0.770). The analyzed 3D-QSAR CoMFA model has demonstrated a good fit, having r(2) value of 0.991 and cross-validated coefficient q(2) value as 0.619. The analysis of CoMFA contour maps provided insight into the possible modification of the molecules for better activity.     

Identification of novel 5-hydroxy-1<Emphasis Type="Italic">H</Emphasis>-indole-3-carboxylates with anti-HBV activities based on 3D QSAR studies

Infection with hepatitis B virus (HBV) is a major cause of liver diseases such as cirrhosis and hepatocellular carcinoma. In our previous studies, we identified indole derivatives that have anti-HBV activities. In this study, we optimize a series of 5-hydroxy-1H-indole-3-carboxylates, which exhibited potent anti-HBV activities, using three-dimensional quantitative structure-activity relationship (3D QSAR) studies with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The lowest energy conformation of compound 3, which exhibited the most potent anti-HBV activity, obtained from systematic search was used as the template for alignment. The best predictions were obtained with the CoMFA standard model (q ² = 0.689, r ² = 0.965, SEE = 0.082, F = 148.751) and with CoMSIA combined steric, electrostatic, hydrophobic and H-bond acceptor fields (q ² = 0.578, r ² = 0.973, SEE = 0.078, F = 100.342). Both models were validated by an external test set of six compounds giving satisfactory prediction. Based on the clues derived from CoMFA and CoMSIA models and their contour maps, another three compounds were designed and synthesized. Pharmacological assay demonstrated that the newly synthesized compounds possessed more potent anti-HBV activities than before (IC₅₀: compound 35a is 3.1 μmol/l, compound 3 is 4.1 μmol/l). Combining the clues derived from the 3D QSAR studies and from further validation of the 3D QSAR models, the activities of the newly synthesized indole derivatives were well accounted for. Furthermore, this showed that the CoMFA and CoMSIA models proved to have good predictive ability.     

Molecular docking guided 3D-QSAR CoMFA analysis of N-4-Pyrimidinyl-1H-indazol-4-amine inhibitors of leukocyte-specific protein tyrosine kinase

Inhibition of leukocyte-specific protein tyrosine kinase (Lck) activity offers one of the approaches for the treatment of T-cell mediated inflammatory disorders including rheumatoid arthritis, transplant rejection and inflammatory bowel disease. To explore the relationship between the structures of the N-4 Pyrimidinyl-1H-indazol-4-amines and their Lck inhibition, 3D-QSAR study using CoMFA analysis have been performed on a dataset of 42 molecules. The bioactive conformation of the template molecule, selected as the most potent molecule 23 from the series was obtained by performing molecular docking at the ATP binding site of Lck, which is then used to build the rest of the molecules in the series. The constructed CoMFA model is robust with of 0.603 and conventional r² of 0.983. The predictive power of the developed model was obtained using a test set of 10 molecules, giving predictive correlation coefficient of 0.921. CoMFA contour analysis was performed to obtain useful information about the structural requirements for the Lck inhibitors which could be utilized in its future design. Figure CoMFA steric contour map. Sterically favored areas (contribution level 80%) are represented by green polyhedra. Sterically disfavored areas (contribution level 20%) are represented by yellow polyhedra. The active molecule 23 shown in capped sticks. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Synthesis and comparative molecular field analysis (CoMFA) of antitumor 3-arylisoquinoline derivatives

In this study a series of 3-arylisoquinoline derivatives were synthesized and cytotoxicity against human melanoma tumor cell evaluated, and a three dimensional quantitative structure—activity relationship was investigated using the comparative molecular field analysis (CoMFA). The results suggested that the electrostatic, steric and hydrophobic factors of 3-arylisoquinolines were strongly correlated with the antitumor activity. Considerable predictive ability (cross-validated r² as high as 0.721) was obtained through CoMFA.     

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT(7) 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 5HT(7) 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 q(2) (cross validated correlation coefficient) of 0.642, 0.602 and r(2) (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT(7) antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r(2) 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.     

Elucidation of binding mode and three dimensional quantitative structure–activity relationship studies of a novel series of protein kinase B/Akt inhibitors

Protein kinase B (PKB; also known as Akt kinase) is located downstream in the PI-3 kinase pathway. Overexpression and constitutive activation of PKB/Akt leads to human prostate, breast and ovarian carcinomas. A series of 69 PKB/Akt inhibitors were examined to explore their binding modes using FlexX, and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed to provide structural insights into these compounds. CoMFA produced statistically significant results, with cross-validated q ² and non-cross validated correlation r ² coefficients of 0.53 and 0.95, respectively. For CoMSIA, steric, hydrophobic and hydrogen bond acceptor fields jointly yielded ‘leave one out’ q ²  = 0.51 and r ²  = 0.84. The predictive power of CoMFA and CoMSIA was determined using a test set of 13 molecules, which gave correlation coefficients, of 0.58 and 0.62, respectively. Molecular docking revealed that the binding modes of these molecules in the ATP binding sites of the Akt kinase domain were very similar to those of the co-crystallized ligand. The information obtained from 3D contour maps will allow the design of more potent and selective Akt kinase inhibitors.