Three-dimensional quantitative structure-activity relationship analyses of piperidine-based CCR5 receptor antagonists

The CCR5 chemokine receptor has recently been found to play a crucial role in the viral entry stage of HIV infection and has therefore become an attractive potential target for anti-HIV therapeutics. On the other hand, the lack of CCR5 crystal structure data has impeded the development of structure-based CCR5 antagonist design. In this paper, we compare two three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) methods: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) on a series of piperidine-based CCR5 antagonists as an alternative approach to investigate the interaction between CCR5 antagonists and their receptor. Superimposition of antagonist structures was performed using two alignment rules: atomic/centroid rms fit and rigid body field fit techniques. The 3D QSAR models were derived from a training set of 72 compounds, and were found to have predictive capability for a set of 19 holdout test compounds. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to biological activity in this series of compounds. Further analyses of these interaction-field contour maps also showed a high level of internal consistency.     

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.     

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.     

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.     

Key features for designing phosphodiesterase-5 inhibitors

Phosphodiesterase superfamily is the key regulator of 3',5'-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q(2), and non cross-validated coefficient, r(2), values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate. Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).     

3D-QSAR,molecular docking,and molecular dynamic simulations for prediction of new Hsp90 inhibitors based on isoxazole scaffold

Heat shock protein 90(Hsp90), as a molecular chaperone, play a crucial role in folding and proper function of many proteins. Hsp90 inhibitors containing isoxazole scaffold are currently being used in the treatment of cancer as tumor suppressers. Here in the present studies, new compounds based on isoxazole scaffold were predicted using a combination of molecular modeling techniques including three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamic (MD) simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were also done. The steric and electrostatic contour map of CoMFA and CoMSIA were created. Hydrophobic, hydrogen bond donor and acceptor of CoMSIA model also were generated, and new compounds were predicted by CoMFA and CoMSIA contour maps. To investigate the binding modes of the predicted compounds in the active site of Hsp90, a molecular docking simulation was carried out. MD simulations were also conducted to evaluate the obtained results on the best predicted compound and the best reported Hsp90 inhibitors in the 3D-QSAR model. Findings indicate that the predicted ligands were stable in the active site of Hsp90.     

Molecular simulation of a series of benzothiazole PI3Kα inhibitors: probing the relationship between structural features, anti-tumor potency and selectivity

The phosphatidylinositol 3-kinase α (PI3Kα) was genetically validated as a promising therapeutic target for developing novel anticancer drugs. In order to explore the structure-activity correlation of benzothiazole series as inhibitors of PI3Kα, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) were performed on 61 promising molecules to build 3D-QSAR models based on both the ligand- and receptor-based methods. The best CoMFA and CoMSIA models had a cross-validated coefficient r(cv)(2) of 0.618 and 0.621, predicted correlation coefficient r(pred) (2) of 0.812 and 0.83, respectively, proving their high correlative and predictive abilities on both the training and test sets. In addition, docking analysis and molecular dynamics simulation (MD) were also applied to elucidate the probable binding modes of these inhibitors at the ATP binding pocket. Based on the contour maps and MD results, some key structural factors responsible for the activity of this series of compounds were revealed as follows: (1) Ring-A has a strong preference for bulky hydrophobic or aromatic groups; (2) Electron-withdrawing groups at the para position of ring-B and hydrophilic substituents in ring-B region may benefit the potency; (3) A polar substituent like -NHSO(2)- between ring-A and ring-B can enhance the activity of the drug by providing hydrogen bonding interaction with the protein target. The satisfactory results obtained from this work strongly suggest that the developed 3D-QSAR models and the obtained PI3Kα inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and be helpful in future PI3Kα inhibitor design.     

Homology modeling of human CCR5 and analysis of its binding properties through molecular docking and molecular dynamics simulation

In this study, homology modeling, molecular docking and molecular dynamics simulation were performed to explore structural features and binding mechanism of some inhibitors of chemokine receptor type 5 (CCR5), and to construct a model for designing new CCR5 inhibitors for preventing HIV attachment to the host cell. A homology modeling procedure was employed to construct a 3D model of CCR5. For this procedure, the X-ray crystal structure of bovine rhodopsin (1F88A) at 2.80? resolution was used as template. After inserting the constructed model into a hydrated lipid bilayer, a 20ns molecular dynamics (MD) simulation was performed on the whole system. After reaching the equilibrium, twenty-four CCR5 inhibitors were docked in the active site of the obtained model. The binding models of the investigated antagonists indicate the mechanism of binding of the studied compounds to the CCR5 obviously. Moreover, 3D pictures of inhibitor-protein complex provided precious data regarding the binding orientation of each antagonist into the active site of this protein. One additional 20 ns MD simulation was performed on the initial structure of the CCR5-ligand 21 complex, resulted from the previous docking calculations, embedded in a hydrated POPE bilayer to explore the effects of the presence of lipid bilayer in the vicinity of CCR5-ligand complex. This article is part of a Special Issue entitled Protein translocation across or insertion into membranes.     

Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1

Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r2, q2(loo) and r2(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r2(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.     

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.     

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.     

Prediction of binding affinities between the human amphiphysin-1 SH3 domain and its peptide ligands using homology modeling, molecular dynamics and molecular field analysis

The SH3 domain of the human protein amphiphysin-1, which plays important roles in clathrin-mediated endocytosis, actin function and signaling transduction, can recognize peptide motif PXRPXR (X is any amino acid) with high affinity and specificity. We have constructed a complex structure of the amphiphysin-1 SH3 domain and a high-affinity peptide ligand PLPRRPPRA using homology modeling and molecular docking, which was optimized by molecular dynamics (MD). Three-dimensional quantitative structure-affinity relationship (3D-QSAR) analyses on the 200 peptides with known binding affinities to the amphiphysin-1 SH3 domain was then performed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMSIA model showed promising predictive power, giving good predictions for about 95% of the peptides in the test set (absolute prediction errors less than 1.0). It was used to validate peptide-SH3 binding structure and provide insight into the structural requirements for binding of peptides to SH3 domains. Finally, MD simulations were performed to analyze the interaction between the SH3 domain and another peptide GFPRRPPPRG that contains with the PXRPXsR (s represents residues with small side chains) motif. MD simulations demonstrated that the binding conformation of GFPRRPPPRG is quite different from that of PLPRRPPRAA especially the four residues at the C terminal, which may explain why the CoMSIA model cannot give good predictions on the peptides of the PXRPXsR motif. Because of its efficiency and predictive power, the 3D-QSAR model can be used as a scoring filter for predicting peptide sequences bound to SH3 domains.     

Key Features for Designing Phosphodiesterase-5 Inhibitors

Abstract

Phosphodiesterase superfamily is the key regulator of 3′,5′-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q², and non cross-validated coefficient, r², values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate.

Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).     

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.     

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.     

3-D-QSAR CoMFA and CoMSIA studies on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists. The best CoMFA and CoMSIA models that were generated using atom based alignment from a training set of twenty five tetrahydrofuroyl-L-phenylalanine derivatives, are six-component models with good statistics; CoMFA: r(2)(cv)=0.366, r(2)=0.983, s=0.099, F=172.661 and PRESS=4.435; CoMSIA: r(2)(cv)=0.528, r(2)=0.995, s=0.054, F=577.87 and PRESS=3.563. Both of these 3-D-QSAR models were validated using a test set of eleven compounds, whose predicted pIC(50) values fall within one log unit of the actual pIC(50). The contour diagrams obtained for the various CoMFA and CoMSIA field contributions can be mapped back onto structural features to explain the activity trends of the molecules analysed. Based on the spatial arrangement of the various field contributions, novel molecules with improved activity can be designed.     

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)     

Molecular docking and 3-D-QSAR studies on the possible antimalarial mechanism of artemisinin analogues

Artemisinin (Qinghaosu) is a natural constituent found in Artemisia annua L, which is an effective drug against chloroquine-resistant Plasmodium falciparum strains and cerebral malaria. The antimalarial activities of artemisinin and its analogues appear to be mediated by the interactions of the drugs with hemin. In order to understand the antimalarial mechanism and the relationship between the physicochemical properties and the antimalarial activities of artemisinin analogues, we performed molecular docking simulations to probe the interactions of these analogues with hemin, and then performed three-dimensional quantitative structure-activity relationship (3-D-QSAR) studies on the basis of the docking models employing comparative molecular force fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Molecular docking simulations generated probable 'bioactive' conformations of artemisinin analogues and provided a new insight into the antimalarial mechanism. The subsequent partial least squares (PLS) analysis indicates that the calculate binding energies correlate well with the experimental activity values. The CoMFA and CoMSIA models based on the bioactive conformations proved to have good predictive ability and in turn match well with the docking result, which further testified the reliability of the docking model. Combining these results, that is molecular docking and 3-D-QSAR, together, the binding model and activity of new synthesized artemisinin derivatives were well explained.     

3D-QSAR,molecular docking and molecular dynamics studies of a series of RORγt inhibitors

The discovery of clinically relevant inhibitors of retinoic acid receptor-related orphan receptor-gamma-t (RORγt) for autoimmune diseases therapy has proven to be a challenging task. In the present work, to find out the structural features required for the inhibitory activity, we show for the first time a three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for a series of novel thiazole/thiophene ketone amides with inhibitory activity at the RORγt receptor. The optimum CoMFA and CoMSIA models, derived from ligand-based superimposition I, exhibit leave-one-out cross-validated correlation coefficient (R²_cv) of .859 and .805, respectively. Furthermore, the external predictive abilities of the models were evaluated by a test set, producing the predicted correlation coefficient (R²_pred) of .7317 and .7097, respectively. In addition, molecular docking analysis was applied to explore the binding modes between the inhibitors and the receptor. MD simulation and MM/PBSA method were also employed to study the stability and rationality of the derived conformations, and the binding free energies in detail. The QSAR models and the results of molecular docking, MD simulation, binding free energies corroborate well with each other and further provide insights regarding the development of novel RORγt inhibitors with better activity.