Comparative Molecular Similarity Analysis Research Articles

Computational Integration of TRPV4 Antagonists: 3D QSAR, Molecular Docking, Molecular Dynamics Simulations, ADME/Tox, and Retrosynthesis studies

AbstractTRPV4 antagonists that could be potential drugs for treatment and management of neuropathic pain. This study was conducted to systematically design several amino derivatives of 2,4′‐dimethyl‐[4,5′‐bithiazol]‐2‐yl as antagonists. In silico computational methods, such as 3D quantitative structure‐activity relationship modeling (3D‐QSAR), molecular docking, and pharmacokinetic property assessment (ADMET), were used to discover new compounds with strong antagonistic activity. The best‐performing 3D‐QSAR model was developed using a partial least squares approach and comparative molecular similarity analysis (CoMSIA). The developed model demonstrated an excellent ability to correlate and predict properties (R2 = 0.932, Q2 = 0.620, and SEE = 0.109). It was observed that variations in biological activity were significantly influenced by interactions with steric, electrostatic, and hydrophobic fields. Molecular docking was used to validate the 3D‐QSAR methods and to explain the binding site and interactions between the most active ligands and the receptor. Based on these results, a new series of compounds was predicted. The best‐anchored molecules underwent MD simulation to confirm their dynamic behavior and stability, and they also underwent retrosynthetic analysis to assist in their synthesis.

Comparative binding affinity analysis of dual CDK2/FLT3 inhibitors

AbstractSelective inhibition of cyclin‐dependent kinase 2 (CDK2) using small molecules is gaining popularity for the treatment of certain types of acute myeloid leukemia (AML). In this study, we used different molecular modeling techniques to investigate the structure–activity relationship (SAR) and binding modalities of dual CDK2/FLT3 inhibitors. The key chemical characteristics of the 3H‐pyrazolo[4,3‐f]quinoline derivatives were highlighted as descriptive colored contours using comparative molecular similarity analysis (CoMSIA). Modifying chemical groups in existing compounds along these contours could improve CDK2 selectivity over FMS‐like tyrosine kinase 3 (FLT3). We determined the ligand affinities for CDK2 by estimating the binding free energy using molecular mechanics generalized Born surface area (MM‐GBSA) and umbrella sampling (US) simulations. Reasonable correlations were found between the computed and experimental binding energies, suggesting that MM‐GBSA and US can be used to reliably predict the binding affinities of new compounds in the more potent CDK2 drug development process.

In silico Modeling of Curcumin Based Sulfonamides Inhibitors of the Human trans-Membrane Carbonic Anhydrase Isozyme, hCA IX by CoMSIA

Carbonic anhydrases, hCAs IX and XII are applied as the markers of progression of the disease in many oxygen deficient tumours and their specially manoeuvred inhibition is directly related to containing the growth of both primary tumours and tumour growth of secondary nature. Ligand-based quantitative structure-activity relationship (QSAR) studies were carried out on curcumin related, sulphonamide derivatives as inhibitors of human trans-membrane carbonic anhydrase isozyme, hCA IX by comparative molecular field similarity analysis (CoMSIA) implemented through the SYBYL package. The capacity of the model to predict coveted compound was evaluated using test set of three compounds. The best model created was found to be of choice as it showed a r2 value of 0.811 and a cross validated coefficient q2 value of 0.617 in tripos CoMSIA hydrophobic region. Results of the present study indicated that hydrophobic region factors play an important role in carbonic anhydrase hCA IX inhibition for compounds.

Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR.

Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.

Understanding the structural features of JAK2 inhibitors: a combined 3D-QSAR, DFT and molecular dynamics study.

JAK2 plays a critical role in JAK/STAT signaling pathway and in patho-mechanism of myeloproliferative disorders and autoimmune diseases. Thus, effective JAK2 inhibitors provide a promising opportunity for the pharmaceutical intervention of many diseases. In this work, 3D-QSAR study was performed on a series of 1-amino-5H-pyrido-indole-4-carboxamide derivatives as JAK2 inhibitors to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) models with three different alignment methods. Among the different alignment methods, ligand-based (CoMFA: q2 = 0.676, r2 = 0.979; CoMSIA: q2 = 0.700, r2 = 0.953) and pharmacophore-based alignment (CoMFA: q2 = 0.710, r2 = 0.982; CoMSIA: q2 = 0.686, r2 = 0.960) has produced better statistical results when compared to receptor-based alignment (CoMFA: q2 = 0.507, r2 = 0.979; CoMSIA: q2 = 0.544, r2 = 0.917). Statistical parameters indicated that data are well fitted and have high predictive ability. The presence of electrostatic and hydrophobic field is highly desirable for potent inhibitory activity, and the steric field plays a minor role in modulating the activity. The contour analysis indicates ARG980, ASN981, ASP939 and LEU937 have more possibility of interacting with bulky, hydrophobic groups in pyrido and positive and negative groups in pyrazole ring. Based on our findings, we have designed sixteen molecules and predicted its activity and drug-like properties. Subsequently, molecular docking, molecular dynamics and DFT calculations were performed to evaluate its potency.

Toward a better understanding of the interaction between somatostatin receptor 2 and its ligands: a structural characterization study using molecular dynamics and conceptual density functional theory

This study is a part of the extensive research intending to provide the structural insights on somatostatin and its receptor. Herein, we have studied the structural complexity involved in the binding of somatostatin receptor 2 (SSTR2) with its agonists and antagonist. A 3D QSAR study based on comparative molecular field analysis and comparative molecular similarity analysis (CoMSIA) discerned that a SSTR2 ligand with electronegative, less-bulkier, and hydrogen atom donating/accepting substitutions is important for their biological activity. A conceptual density functional theory (DFT) study was followed to study the chemical behavior of the ligands based on the molecular descriptors derived using the Fukui’s molecular orbital theory. We have performed molecular dynamics simulations of receptor–ligand complexes for 100 ns to analyze the dynamic stability of the backbone Cα atoms of the receptor and strength and approachability of the receptor–ligand complex. The findings of this study could be efficacious in the further studies understanding intricate structural features of the somatostatin receptors and in discovering novel subtype-specific ligands with higher affinity.Communicated by Ramaswamy H. Sarma

Design, synthesis and biological evaluation of novel oseltamivir derivatives as potent neuraminidase inhibitors

Neuraminidase (NA) is one of the particular potential targets for novel antiviral therapy. In this work, a series of neuraminidase inhibitors with the cyclohexene scaffold were studied based upon the combination of 3D-QSAR, molecular docking, and molecular dynamics techniques. The results indicate that the built 3D-QSAR models yield reliable statistical information: the correlation coefficient (r2) and cross-validation coefficient (q2) of CoMFA (comparative molecular field analysis) are 0.992 and 0.819; the r2 and q2 of CoMSIA (comparative molecular similarity analysis) are 0.992 and 0.863, respectively. Molecular docking and MD simulations were conducted to confirm the detailed binding mode of enzyme-inhibitor system. The new NA inhibitors had been designed, synthesized, and their inhibitory activities against group-1 neuraminidase were determined. One agent displayed excellent neuraminidase inhibition, with IC50 value of 39.6 μM against NA, while IC50 value for oseltamivir is 61.1 μM. This compound may be further investigated for the treatment of infection by the new type influenza virus.

Exploring in house glutamate inhibitors of matrix metalloproteinase-2 through validated robust chemico-biological quantitative approaches

Matrix metalloproteinase-2 (MMP-2) is established as one of the most important metalloenzymes for targeting cancer. However, homologous MMP-9 of the gelatinase family is implicated as an antitarget of cancer. Therefore, it is an important and challenging task to achieve MMP-2 selectivity over MMP-9. In this article, robust validated chemico-biological quantitative approaches were conducted on a series of in house glutamate-based selective MMP-2 inhibitors over MMP-9 for further refinement of our MMP-2 inhibitor designing approach. The two-dimensional quantitative structure-activity relationship (2D-QSAR) study suggested that arylsulfonamide moiety was better than arylcarboxamide function, which in turn, supported by the hologram QSAR (HQSAR), 3D-QSAR comparative molecular field analysis (CoMFA), and comparative molecular similarity analysis (CoMSIA) studies. Regarding the MMP-2 selectivity, glutamines were better than isoglutamines as evidenced by the quantitative activity-activity relationship (QAAR) and molecular docking studies. Favorable hydrophobic and steric features of aryl function directed towards the S1′ pocket were also well attributed. Naphthyl and p-bromophenoxyphenyl moieties in place of biphenyl function were found to be unfavorable for MMP-2 inhibition and selectivity over MMP-9. Linear or cyclic aliphatic group directed towards the S2′ pocket was favorable, whereas branching was unfavorable for MMP-2 inhibition and selectivity. The importance of biphenyl and 3,5-bistrifluoromethylbenzyl functions directed towards the S1′ and S2′ pockets, respectively, was well attributed for potent MMP-2 inhibition and selectivity over MMP-9.

An integrated ligand-based modelling approach to explore the structure-property relationships of influenza endonuclease inhibitors

Influenza endonuclease plays important role in the viral transcription and translation processes. Inhibition of endonuclease enzyme may be an interesting choice to restrict influenza infection. This current study deals with validated multi-chemometric modelling approaches namely regression-based and classification-based quantitative structure-activity relationships (QSARs), hologram QSAR, comparative molecular similarity analysis (CoMSIA), Open3DQSAR study and pharmacophore mapping to identify the structural and physicochemical requirements along with the chemico-biological interactions of pyridinones and pyranones for anti-endonuclease activity. The results suggest that the pyridinone scaffold is more preferable than the pyranone ring. The keto function at 4th position and aryl tetrazole substitution at 1st position of the parent moiety may be important for endonuclease inhibition. Hydroxyl group at 5th position of the parent ring may act as hydrogen bond acceptor feature. The steric substituent is suitable at 2nd position whereas hydrophobic substitution is found to be unfavourable at this position. Bulky hydrophobic substituents are not preferred at the 3rd position of the parent moiety. The information revealed from these integrated ligand-based modelling methods may provide useful informations for designing newer potential anti-influenza agents in future.

Probing the structural requirements for thyroid hormone receptor inhibitory activity of sulfonylnitrophenylthiazoles (SNPTs) using 2D-QSAR and 3D-QSAR approaches

A series of sulfonylnitrophenylthiazoles derivatives were identified as effective targeting agents that block the interaction of the thyroid hormone receptor with its coactivators. In this work, in order to analyze the structure-activity relationship of these inhibitors and investigate the structural requirements for thyroid hormone receptor inhibitory activity, new statistically validated in silico models adopting different molecular descriptors were established. The two-dimensional quantitative structure-activity relationship models were developed using multiple linear regression method, which show both significant statistical quality and predictive ability (R 2 = 0.939, Q 2 = 0.622 for thyroid hormone receptor β; R 2 = 0.862, Q 2 = 0.763 for thyroid hormone receptor α), and different molecular descriptors were included, namely R2e, H5U, EEigo4r and Ram for thyroid hormone receptor β, MATS1P, IC2 and R5e+ for thyroid hormone receptor α. The optimum comparative molecular field analysis models were established using the template ligand-based alignment, which show satisfactory linear correlations (thyroid hormone receptor β: R 2 cv = 0.577, R 2 pred = 0.8013; thyroid hormone receptor α: R 2 cv = 0.549, R 2 pred = 0.8639). In addition, the R 2 cv of 0.543, R 2 pred of 0.8523 for thyroid hormone receptor β and R 2 cv of 0.560, R 2 pred of 0.8695 for thyroid hormone receptor α have been observed when comparative molecular similarity analysis fields were applied. All the developed statistical models give satisfactory results with accurate fitting and strong predictive abilities. Moreover, the contour maps provide an intuitive understanding of the structural requirements for the inhibitors. In conclusion, these data can provide some meaningful theoretical references to understand the factors influencing the inhibitory activity and direct the molecular design of novel inhibitors with increased activity.

In silico study on β-aminoketone derivatives as thyroid hormone receptor inhibitors: a combined 3D-QSAR and molecular docking study

In order to explore the structure–activity correlation of a series of β-aminoketone analogs as inhibitors of thyroid hormone receptor (TR), a set of three-dimensional quantitative structure–activity relationship (3D-QSAR) models based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA), for the first time, were developed in the present work. The best CoMFA model with steric and electrostatic fields exhibited , for TRβ, and , for TRα. 3D contour maps produced from the optimal models were further analyzed individually, which provide the areas in space where interactive fields would affect the inhibitory activity. In addition, the binding modes of inhibitors at the active site of TRs were examined using molecular docking, the results indicated that this series of inhibitors fit into the active site of TRs by forming hydrogen bonding and electrostatic interactions. The docking studies also revealed that Leu305, Val458 for TRβ, and Asp407 for TRα are showing hydrogen bonds with the most active inhibitors. In any case, the 3D-QSAR models combined with the binding information will serve as a useful approach to explore the chemical space for improving the activity of TRβ and TRα inhibitors.

Quantitative Relationships Between the Cytotoxicity of Flavonoids on the Human Breast Cancer Stem-Like Cells MCF7-SC and Their Structural Properties.

As some breast cancer-related deaths can be attributed to the metastasis of cancer stem cells, chemotherapeutic agents targeting breast cancer stem cells are of interest as a potential treatment. Flavonoids that exhibit cytotoxicity on breast cancer stem cells have rarely been observed. Thus, the objective of this study was to measure potential cytotoxic effects of 42 different flavonoids on the human breast cancer stem-like cell line, MCF7-SC. The relationship between flavonoid structural properties and cytotoxicity has not been reported previously; therefore, we determined quantitative structure-activity relationships using both comparative molecular field analysis and comparative molecular similarity analysis. Further biological experiments including Western blot analysis, flow cytometry, and immunofluorescence microscopy were also conducted on the most cytotoxic 8-chloroflavanone.

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

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

Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors

Matrix metalloproteinase-2 (MMP-2) is a potential target in metastases. Regression (conventional 2D QSAR) and classification (recursive partitioning (RP), Bayesian modelling) QSAR, pharmacophore mapping and 3D QSAR (comparative molecular field analysis and comparative molecular similarity analysis) were performed on 202 MMP-2 inhibitors. Quality of the regression models was justified by internal (Q(2) ) and external (R(2) Pred ) cross-validation parameters. Stepwise regression was used to develop linear model (Q(2) = 0.822, R(2) Pred = 0.667). Genetic algorithm developed linear (Q(2) = 0.845, R(2) Pred = 0.638) and spline model (Q(2) = 0.882, R(2) Pred = 0.644). The RP and Bayesian models showed cross-validated area under receiver operating characteristic curve (AUCROC _ CV ) of 0.805 and 0.979 respectively. QSAR models depicted importance of descriptors like five-membered rings, fractional positively charged surface area, lipophilocity and so on. Higher molecular volume was found to be detrimental. Pharmacophore mapping was performed with two tools - Hypogen and PHASE. Both models indicated that one hydrophobic and three hydrogen bond acceptor features are essential. The Pharmacophore-aligned structures were used for CoMFA (Q(2) of 0.586 and R(2) Pred of 0.689) and CoMSIA (Q(2) of 0.673 and R(2) Pred of 0.758), results of which complied with the other analyses. All modelling techniques were compared to each other. The current study may help in designing novel MMP-2 inhibitors.

Cholesteryl ester transfer protein inhibitors in coronary heart disease: Validated comparative QSAR modeling of N, N-disubstituted trifluoro-3-amino-2-propanols

Cholesteryl ester transfer protein (CETP) converts high density lipoprotein cholesterol to low density lipoproteins. It is a promising target for treatment of coronary heart disease. Two dimensional quantitative structure activity relationship (2D-QSAR), hologram QSAR (HQSAR) studies and comparative molecular field analysis (CoMFA) as well as comparative molecular similarity analysis (CoMSIA) were performed on 104 CETP inhibitors. The statistical qualities of generated models were justified by internal and external validation, i.e., q2 and R2pred respectively. The best 2D-QSAR model was obtained with q2 and R2pred values of 0.794 and 0.796 respectively. The 2D-QSAR study suggests that unsaturation, branching and van der Waals volumes may play important roles. The HQSAR model showed q2 and R2pred values of 0.628 and 0.550 respectively. Similarly, CoMFA model showed q2 and R2pred values of 0.707 and 0.755 respectively whereas CoMSIA model was obtained with q2 and R2pred values of 0.696 and 0.703 respectively. CoMFA and CoMSIA studies indicate that steric factors are important at substituted phenoxy and tetrafluoroethoxy groups whereas electropositive factors play important role at difluoromethyl group. The results of 3D-QSAR studies validate those of 2D-QSAR and HQSAR studies as well as the earlier observed SAR data. Current work may help to develop better CETP inhibitors.

Molecular design of new aggrecanases-2 inhibitors

Aggrecanases-2 is a very important potential drug target for the treatment of osteoarthritis. In this study, a series of known aggrecanases-2 inhibitors was analyzed by the technologies of three-dimensional quantitative structure–activity relationships (3D-QSAR) and molecular docking. Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established. Molecular docking was employed to explore the details of the interaction between inhibitors and aggrecanases-2 protein. According to the analyses for these models, several new potential inhibitors with higher activity predicted were designed, and were supported by the simulation of molecular docking. This work propose the fast and effective approach to design and prediction for new potential inhibitors, and the study of the interaction mechanism provide a better understanding for the inhibitors binding into the target protein, which will be useful for the structure-based drug design and modifications.

The discovery of inhibitors of Fas-mediated cell death pathway using the combined computational method

In this study, pharmacophore and 3D-QSAR models were developed for analogues of 3-substituted-benzofuran-2-carboxylate as inhibitors of Fas-mediated cell death pathways. Our pharmacophore model has good correspondence with experimental results and can explain the variance in biological activities coherently with respect to the structure of the data set compounds. The predictive power for our synthesized compounds were 0.96 for the pharmacophore model, 0.58 for the comparative molecular field analysis (CoMFA) model, and 0.57 for the comparative molecular similarity analysis (CoMSIA) model.

Exploring structural requirements of aurone derivatives as antimalarials by validated DFT-based QSAR, HQSAR, and COMFA–COMSIA approach

Chloroquine resistance is nowadays a great problem in malaria. Aurone derivatives were effective against chloroquine resistant parasite. Validated density functional theory (DFT)-based chemometric modeling, hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity analysis (CoMSIA) studies were conducted on 35 aurone derivatives having antimalarial activity. 2D-QSAR models were developed on the training sets by Y-based ranking method. This model was validated on 50 pairs of the test and the training sets by k-Means cluster analysis method. HQSAR, CoMFA, and CoMSIA models were validated by standard techniques and each method validates the DFT-based 2D-QSAR study and in turn validates the earlier observed structural activity relationship data as well as each other. DFT-based 2D-QSAR model suggests that the increase of Mulliken charge at C14 and HOMO density located on C11 may be conducive to antimalarial activity. Ethyl group attached to C14 and the increase of the value of chemical potential may be beneficial for antimalarial activity. Methoxy fragment is important for better antimalarial activity by HQSAR study. CoMFA analysis shows a favorable steric green region is located near C14 whereas the unfavorable yellow region is far away from C14. A large blue region located near C14 indicates the positively charged groups are favorable at this position. CoMSIA steric features correlates well with the CoMFA steric features. CoMSIA study suggests the bulky hydrophobic substitution at C14 is necessary for antimalarial activity. These results may be utilized to obtain potential antimalarial molecules.

Characterization of Binding Mode for Human Coagulation Factor XI (FXI) Inhibitors

The human coagulation factor XI (FXI) is a serine protease that plays a significant role in blocking of the blood coagulation cascade as an attractive antithrombotic target. Selective inhibition of FXIa (an activated form of factor XI) disrupts the intrinsic coagulation pathway without affecting the extrinsic pathway or other coagulation factors such as FXa, FIIa, FVIIa. Furthermore, targeting the FXIa might significantly reduce the bleeding side effects and improve the safety index. This paper reports on a docking-based three dimensional quantitative structure activity relationship (3D-QSAR) study of the potent FXIa inhibitors, the chloro-phenyl tetrazole scaffold series, using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Due to the characterization of FXIa binding site, we classified the alignment of the known FXIa inhibitors into two groups according to the docked pose: S1-S2-S4 and S1-S1'-S2'. Consequently, highly predictive 3D-QSAR models of our result will provide insight for designing new potent FXIa inhibitors.

Activity landscape analysis, CoMFA and CoMSIA studies of pyrazole CB1 antagonists

Obesity and the metabolic syndrome are pandemic diseases with high morbidity and mortality. With the aim of discovering novel therapies for those diseases, the cannabinoid receptor 1 (CB1), which has been validated as a target for treating appetitive disorders, is now considered a novel target for the design of anti-obesity compounds. Our main goal was to determine the activity landscape of pyrazole derivatives and to develop reliable three-dimensional quantitative structure–activity relationship (3D-QSAR) models. Structure–activity similarity (SAS) maps of pyrazole analogs acting as antagonists of CB1 were constructed in order to identify activity cliffs, compounds that have high structural similarity with the rest of the compound set, but low activity similarity. According to the SAS maps, one molecule was identified as an outlier and before comparative molecular field analysis (COMFA) and comparative molecular similarity analysis (CoMSIA) 3D-QSAR models were derived. The best models resulted in an r 2 value of 0.992 and a q 2 of 0.766 for CoMFA and an r 2 of 0.983 and a q 2 of 0.681 for CoMSIA. Contour plots identified that the R3 position at the pyrazole moiety is an important feature for the optimization of the binding affinity to the CB1 receptor. According to our results, these models can be a useful tool for the design and prediction of novel CB1 antagonists.