Inhibitory mode of indole-2-carboxamide derivatives against HLGPa: molecular docking and 3D-QSAR analyses

The interaction of a series of indole-2-carboxamide compounds with human liver glycogen phosphorylase a (HLGPa) have been studied employing molecular docking and 3D-QSAR approaches. The Lamarckian Genetic Algorithm (LGA) of AutoDock 3.0 was employed to locate the binding orientations and conformations of the inhibitors interacting with HLGPa. The binding models were demonstrated in the aspects of inhibitor's conformation, subsite interaction, and hydrogen bonding. The very similar binding conformations of these inhibitors show that they interact with HLGPa in a very similar way. Good correlations between the calculated interaction free energies and experimental inhibitory activities suggest that the binding conformations of these inhibitors are reasonable. The structural and energetic differences in inhibitory potencies of indole-2-carboxamide compounds were reasonably explored. Using the binding conformations of indole-2-carboxamides, consistent and highly predictive 3D-QSAR models were developed by CoMFA and CoMSIA analyses. The q2 values are 0.697 and 0.622 for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by four compounds that were not included in the training set. Mapping these models back to the topology of the active site of HLGPa leads to a better understanding of the vital indole-2-carboxamide-HLGPa interactions. Structure-based investigations and the final 3D-QSAR results provide clear guidelines and accurate activity predictions for novel inhibitor design.     

Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors

Three-dimensional quantitative structure-activity relationship (QSAR) studies were conducted on two classes of recently explored compounds with known YopH inhibitory activities. Docking studies were employed to position the inhibitors into the YopH active site to determine the probable binding conformation. Good correlations between the predicated binding free energies and the inhibitory activities were found for two subsets of phosphate mimetics: alpha-ketocarboxylic acid and squaric acid (R2=0.70 and 0.68, respectively). The docking results also provided a reliable conformational alignment scheme for 3D-QSAR modeling. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed based on the docking conformations, giving q2 of 0.734 and 0.754 for CoMFA and CoMSIA models, respectively. The 3D-QSAR models were significantly improved after removal of an outlier (q2=0.829 for CoMFA and q2=0.837 for CoMSIA). The predictive ability of the models was validated using a set of compounds that were not included in the training set. Mapping the 3D-QSAR models to the active site of YopH provides new insight into the protein-inhibitor interactions for this enzyme. These results should be applicable to the prediction of the activities of new YopH inhibitors, as well as providing structural implications for designing potent and selective YopH inhibitors as antiplague agents.     

3D-QSAR studies on fluoropyrrolidine amides as dipeptidyl peptidase IV inhibitors by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses using CoMFA and CoMSIA methods were conducted on a series of fluoropyrrolidine amides as dipeptidyl peptidase IV (DP-IV) inhibitors. The selected ligands were docked into the binding site of the 3D model of DP-IV using the GOLD software, and the possible interaction models between DP-IV and the inhibitors were obtained. Based on the binding conformations of these fluoropyrrolidine amides and their alignment inside the binding pocket of DP-IV, predictive 3D-QSAR models were established by CoMFA and CoMSIA analyses, which had conventional r ² and cross-validated coefficient values () up to 0.982 and 0.555 for CoMFA and 0.953 and 0.613 for CoMSIA, respectively. The predictive ability of these models was validated by six compounds that were in the testing set. Structure-based investigations and the final 3D-QSAR results provide the guide for designing new potent inhibitors.     

Docking-based 3D-QSAR study for 11beta-HSD1 inhibitors

11beta-Hydroxysteroid dehydrogenase (11beta-HSD) enzymes catalyze the conversion of biologically inactive 11-ketosteroids into their active 11beta-hydroxy derivatives and vice versa. 11beta-HSD1 has been studied as a potential treatment for metabolic disease such as diabetes and obesity. To find correlation between 11beta-HSD1 and inhibitors, three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on 70 inhibitors, based on molecular docking conformations obtained by using FlexX-Pharm. The studies include comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Based on the docking results, highly predictive 3D-QSAR models were developed with q(2) values of 0.543 and 0.519 for CoMFA and CoMSIA, respectively. A comparison of the 3D-QSAR field contributions with the structural features of the binding site showed good correlation between the two analyses. Therefore, these results should be useful to the prediction of the activities of new 11beta-HSD1 inhibitors.     

3D-QSAR and docking studies of aldehyde inhibitors of human cathepsin K

In order to better understand the structural and chemical features of human cathepsin K (CatK), which is an important cysteine protease in the pathogenesis of osteoporosis, the 3D-QSAR (CoMFA) studies were conducted on recently explored aldehyde compounds with known CatK inhibitory activities. The genetic algorithm of GOLD2.2 has been employed to position 59 aldehyde compounds into the active sites of CatK to determine the probable binding conformation. Good correlations between the predicted binding free energies and the experimental inhibitory activities suggested that the identified binding conformations of these potential inhibitors are reliable. The docking results also provided a reliable conformational alignment scheme for 3D-QSAR model. Based on the docking conformations, highly predictive comparative molecular field analysis (CoMFA) was performed with q2 value of 0.723. The predictive ability was validated by some compounds that were not included in the training set. Furthermore, the CoMFA model was mapped back to the binding sites of CatK, to get a better understanding of vital interactions between the aldehyde compounds and the protease. The CoMFA field distributions are in good agreement with the structural characteristics of the binding groove of the CatK, which suggested that the n-Bu in R4 position is the favor group substitute at P1 and moderate groups in R2 group are required on P2 substitute. In addition, 3D-QSAR results also demonstrated that aldehyde is an important pharmacophore because of electrostatic effect. These results, together with the good correlations between the inhibitory activities and the binding free energies predicted by GOLD2.2, demonstrated the power of combining docking/QSAR approach to explore the probable binding conformations of compounds at the active sites of the protein target, and further provided useful information in understanding the structural and chemical features of CatK in designing and finding new potential inhibitors.     

Molecular docking and 3D-QSAR study on 4-(1H-indazol-4-yl) phenylamino and aminopyrazolopyridine urea derivatives as kinase insert domain receptor (KDR) inhibitors

Vascular endothselial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as new promising targets for the design of novel anticancer agents. It is reported that 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives exhibit potent inhibitory activities toward KDR. To investigate how their chemical structures relate to the inhibitory activities and to identify the key structural elements that are required in the rational design of potential drug candidates of this class, molecular docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were performed on 78 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives as KDR inhibitors. Surflex-dock was used to determine the probable binding conformations of all the compounds at the active site of KDR. As a result, multiple hydrophobic and hydrogen-bonding interactions were found to be two predominant factors that may be used to modulate the inhibitory activities. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were developed based on the docking conformations. The CoMFA model produced statistically significant results with the cross-validated correlation coefficient q² of 0.504 and the non-cross-validated correlation coefficient r² of 0.913. The best CoMSIA model was obtained from the combination of steric, electrostatic and hydrophobic fields. Its q² and r² being 0.595 and 0.947, respectively, indicated that it had higher predictive ability than the CoMFA model. The predictive abilities of the two models were further validated by 14 test compounds, giving the predicted correction coefficients r_pred² of 0.727 for CoMFA and 0.624 for CoMSIA, respectively. In addition, the CoMFA and CoMSIA models were used to guide the design of a series of new inhibitors of this class with predicted excellent activities. Thus, these models may be used as an efficient tool to predict the inhibitory activities and to guide the future rational design of 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives-based novel KDR inhibitors with potent activities.     

Receptor based 3D-QSAR to identify putative binders of Mycobacterium tuberculosis Enoyl acyl carrier protein reductase

In the current study, the applicability and scope of 3D-QSAR models (CoMFA and CoMSIA) to complement virtual screening using 3D pharmacophore and molecular docking is examined and applied to identify potential hits against Mycobacterium tuberculosis Enoyl acyl carrier protein reductase (MtENR). Initially CoMFA and CoMSIA models were developed using series of structurally related arylamides as MtENR inhibitors. Docking studies were employed to position the inhibitors into MtENR active site to derive receptor based 3D-QSAR models. Both CoMFA and CoMSIA yielded significant cross validated q² values of 0.663 and 0.639 and r² values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r² value of 0.882 and 0.875 for CoMFA and CoMSIA. The contour maps from 3D-QSAR models in combination with docked binding structures help to better interpret the structure activity relationship. Integrated with CoMFA and CoMSIA predictive models structure based (3D-pharmacophore and molecular docking) virtual screening have been employed to explore potential hits against MtENR. A representative set of 20 compounds with high predicted IC₅₀ values were sorted out in the present study.     

3D-QSAR study of 8-azabicyclo[3.2.1] octane analogs antagonists of cholinergic receptor

3D-QSAR models of Comparative of Molecular Field Analysis (CoMFA) and Comparative of Molecular Similarities Indices Analysis (CoMSIA) of 20 8-azabicyclo[3.2.1] octane (potent muscarinic receptor blocker) was performed. These benztropine analogs were optimized using ligand based alignment method. The conventional ligand-based 3D-QSAR studies were performed based on the low energy conformations employing database alignment rule. The ligand-based model gave q² value 0.819 and 0.810 and r² value 0.991 and 0.988 for CoMFA and CoMSIA, respectively, and the predictive ability of the model was validated. Results indicate that the CoMFA and CoMSIA models could be reliable model which may be used in the design of novel muscarinic antagonists as leads.     

Elucidating inhibitory models of the inhibitors of epidermal growth factor receptor by docking and 3D-QSAR

Epidermal growth factor receptor (EGFR) protein tyrosine kinases (PTKs) are attractive targets for anti-tumor drug design. Although thousands of their ligands have been studied as potential inhibitors against PTKs, there is no QSAR study that covers different kinds of inhibitors with observable structural diversity. However, by using this approach, we could mine far more useful information. Hence in order to better understand the binding model and the relationship between the physicochemical properties and the inhibitory activities of different kind of various inhibitors, molecular docking and 3D-QSAR, viz. CoMFA and CoMSIA, were combined to study 124 reported inhibitors with different scaffolds. Based on the docked binding conformations, highly reliable and predictive 3D-QSAR models were derived, which reveal how steric, electrostatic, and hydrophobic interactions contribute to inhibitors' bioactivities. This result also demonstrates that it is possible to include different kinds of inhibitors with observable structural diversity into one 3D-QSAR study. Therefore, this study not only casts light on binding mechanism between EGFR and its inhibitors, but also provides new hints for de novo design of new EGFR inhibitors with observable structural diversity.     

3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of falcipain-3 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 42 molecules served to establish the QSAR models. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients r(cv)(2) (q(2)) of 0.549 and 0.608, and conventional correlation coefficients (r(2)) of 0.976 and 0.932, respectively. An independent test set of 12 molecules validated the external predictive power of both models with predicted correlation coefficients (r(pred)(2)) for CoMFA and CoMSIA as 0.697 and 0.509, respectively. The docking of inhibitors into falcipain-3 active site using GOLD software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of falcipain-3 active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved falcipain-3 inhibitors.     

Three-dimensional quantitative structure–activity relationship study on paullones as CDK inhibitors using CoMSIA and CoMFA

3D-QSAR studies were conducted on a series of paullones as CDK inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR) methods. Two methods were compared: the widely used comparative molecular field analysis (CoMFA) and the recently reported comparative molecular similarity indices analysis (CoMSIA). Systematic variations of some parameters in CoMSIA and CoMFA were performed to search for the best 3D-QSAR model. The computed results showed that the 3D-QSAR models from CoMSIA were clearly superior to those from CoMFA. Using the best model from CoMSIA analysis, a significant cross-validated q² was obtained and the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The correlation results obtained from CoMSIA were graphically interpreted in terms of field contribution maps allowing physicochemical properties relevant for binding to be easily mapped back onto molecular structures. The features in the CoMSIA contour maps intuitively suggested where to modify a molecular structure in terms of physicochemical properties and functional groups in order to improve its binding affinity, which is very important for improving our understanding of the ligand-receptor interactions and in helping to design compounds with improved activity.     

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis – a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r_loo² values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r² values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r_pred²) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r_pred² of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.     

3D-QSAR and molecular docking studies of azaindole derivatives as Aurora B kinase inhibitors

The Aurora kinases have been regarded as attractive targets for the development of new anticancer agents. Recently a series of azaindole derivatives with Aurora B inhibitory activities were reported. To explore the relationship between the structures of substituted azaindole derivatives and their inhibition of Aurora B, 3D-QSAR and molecular docking studies were performed on a dataset of 41 compounds. 3D-QSAR, including CoMFA and CoMSIA, were applied to identify the key structures impacting their inhibitory potencies. The CoMSIA model showed better results than CoMFA, with r ² _cv value of 0.575 and r ² value of 0.987. 3D contour maps generated from CoMFA and CoMSIA along with the docking binding structures provided enough information about the structural requirements for better activity. Based on the structure-activity relationship revealed by the present study, we have designed a set of novel Aurora B inhibitors that showed excellent potencies in the developed models. Thus, our results allowed us to design new derivatives with desired activities.     

3D-QSAR CoMFA, CoMSIA studies on substituted ureas as Raf-1 kinase inhibitors and its confirmation with structure-based studies

Three-dimensional quantitative structure activity relationship (3D-QSAR) analyses were carried out on 91 substituted ureas in order to understand their Raf-1 kinase inhibitory activities. The studies include Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Models with good predictive abilities were generated with the cross validated r2 (r2cv) values for CoMFA and CoMSIA being 0.53 and 0.44, respectively. The conventional r2 values are 0.93 and 0.87 for CoMFA and CoMSIA, respectively. In addition, a homology model of Raf-1 was also constructed using the crystal structure of the kinase domain of B-Raf isoform with one of the most active Raf-1 inhibitors (48) inside the active site. The ATP binding pocket of Raf-1 is virtually similar to that of B-Raf. Selected ligands were docked in the active site of Raf-1. Molecule 48 adopts an orientation similar to that inside the B-Raf active site. The 4-pyridyl group bearing amide substituent is located in the adenosine binding pocket, and anchored to the protein through a pair of hydrogen bonds with Cys424 involving ring N-atom and amide NH group. The results of best 3D-QSAR model were compared with structure-based studies using the Raf-1 homology model. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for activity of this class of molecules as Raf-1 inhibitors. Based on these results, novel molecules with improved activity can be designed.     

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents

Oxazolidinones exemplified by eprezolid and linezolid are a new class of antibacterials that are active against Gram positive and anaerobic bacteria including methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant Staphylococcus epidermidis (MRSE) and vancomycin resistant enterococci (VRE). In an effort to have a better antibacterial agent in the oxazolidinone class, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of tricyclic oxazolidinones. 3D-QSAR studies were performed using the Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) procedures. These studies were performed using 42 compounds; the QSAR model was developed using a training set of 33 compounds. The predictive ability of the QSAR model was assessed using a test set of 9 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.975 and 0.940 for CoMFA and CoMSIA respectively; similarly, cross-validated coefficient q(2) values of 0.523 and 0.557 for CoMFA and CoMSIA, respectively, were obtained. The CoMFA 3D-QSAR model performed better than the CoMSIA model.     

3D-QSAR studies on tripeptide aldehyde inhibitors of proteasome using CoMFA and CoMSIA methods

The ubiquitin-proteasome pathway plays a crucial role in the regulation of many physiological processes and in the development of a number of major human diseases, such as cancer, Alzheimer's, Parkinson's, diabetes, etc. As a new target, the study on the proteasome inhibitors has received much attention recently. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using comparative molecule field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) techniques were applied to analyze the binding affinity of a set of tripeptide aldehyde inhibitors of 20S proteasome. The optimal CoMFA and CoMSIA models obtained for the training set were all statistically significant with cross-validated coefficients (q(2)) of 0.615, 0.591 and conventional coefficients (r(2)) of 0.901, 0.894, respectively. These models were validated by a test set of eight molecules that were not included in the training set. The predicted correlation coefficients (r(2)) of CoMFA and CoMSIA are 0.944 and 0.861, respectively. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of beta5 subunit of 20S proteasome, which suggests that the 3D-QSAR models built in this paper can be used to guide the development of novel inhibitors of 20S proteasome.     

Structure-based 3D-QSAR studies on thiazoles as 5-HT<Subscript>3</Subscript> receptor antagonists

Structure-based 3D-QSAR studies were performed on 20 thiazoles against their binding affinities to the 5-HT₃ receptor with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The thiazoles were initially docked into the binding pocket of a human 5-HT_3A receptor homology model, constructed on the basis of the crystal structure of the snail acetylcholine binding protein (AChBP), using the GOLD program. The docked conformations were then extracted and used to build the 3D-QSAR models, with cross-validated values 0.785 and 0.744 for CoMFA and CoMSIA, respectively. An additional five molecules were used to validate the models further, giving satisfactory predictive values of 0.582 and 0.804 for CoMFA and CoMSIA, respectively. The results would be helpful for the discovery of new potent and selective 5-HT₃ receptor antagonists.      

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.     

Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses

Docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were conducted on a series of indole amide analogues as potent histone deacetylase inhibitors. The studies include comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Selected ligands were docked into the active site of human HDAC1. Based on the docking results, a novel binding mode of indole amide analogues in the human HDAC1 catalytic core is presented, and enzyme/inhibitor interactions are discussed. The indole amide group is located in the open pocket, and anchored to the protein through a pair of hydrogen bonds with Asp99 O-atom and amide NH group on ligand. Based on the binding mode, predictive 3D-QSAR models were established, which had conventional r2 and cross-validated coefficient values (r(cv)2) up to 0.982 and 0.601 for CoMFA and 0.954 and 0.598 for CoMSIA, respectively. A comparison of the 3D-QSAR field contributions with the structural features of the binding site showed good correlation between the two analyses. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for activity of this class of molecules as HDAC inhibitors. The CoMFA and CoMSIA PLS contour maps and MOLCAD-generated active site electrostatic, lipophilicity, and hydrogen-bonding potential surface maps, as well as the docking studies, provided good insights into inhibitor-HDAC interactions at the molecular level. Based on these results, novel molecules with improved activity can be designed.     

A combined approach of docking and 3D QSAR study of beta-ketoacyl-acyl carrier protein synthase III (FabH) inhibitors

The enzyme FabH catalyzes the initial step of fatty acid biosynthesis via a type II fatty acid synthase. The pivotal role of this essential enzyme combined with its unique structural features and ubiquitous occurrence in bacteria has made it an attractive new target for the development of antibacterial and antiparasitic compounds. Three-dimensional quantitative structure-activity relationship (3D QSAR) studies such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) and docking simulations were conducted on a series of potent benzoylaminobenzoic acids. Docking studies were employed to position the inhibitors into the FabH active site to determine the probable binding conformation. A reasonable correlation between the predicated binding free energy and the inhibitory activity was found. CoMFA and CoMSIA were performed based on the docking conformations, giving q(2) of 0.637 and 0.697 for CoMFA and CoMSIA models, respectively. The predictive ability of the models was validated using a set of compounds that were not included in the training set and progressive scrambling test. Mapping the 3D QSAR models to the active site of FabH related that some important amino acid residues are responsible for protein-inhibitor interaction. These results should be applicable to the prediction of the activities of new FabH inhibitors, as well as providing structural understanding.