Structural Determinants of CX-4945 Derivatives as Protein Kinase CK2 Inhibitors: A Computational Study

Protein kinase CK2, also known as casein kinase-2, is involved in a broad range of physiological events including cell growth, proliferation and suppression of apoptosis which are related to human cancers. A series of compounds were identified as CK2 inhibitors and their inhibitory activities varied depending on their structures. In order to explore the structure-activity correlation of CX-4945 derivatives as inhibitors of CK2, in the present study, a set of ligand- and receptor-based 3D-QSAR models were developed employing Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA). The optimum CoMFA (R(cv) (2) = 0.618, R(pred) (2) = 0.892) and CoMSIA (R(cv) (2) = 0.681, R(pred) (2) = 0.843) models exhibited reasonable statistical characteristics for CX-4945 derivatives. The results indicated that electrostatic effects contributed the most to both CoMFA and CoMSIA models. The combination of docking analysis and molecular dynamics (MD) simulation showed that Leu45, Lys68, Glu81, Val116, Asp175 and Trp176 of CK2 which formed several direct or water-bridged H-bonds with CX-4945 are crucial for CX-4945 derivatives recognition to CK2. These results can offer useful theoretical references for designing more potent CK2 inhibitors.     

In silico identification of structure requirement for novel thiazole and oxazole derivatives as potent fructose 1,6-bisphosphatase inhibitors

Fructose 1,6-bisphosphatase (FBPase) has been identified as a drug discovery target for lowering glucose in type 2 diabetes mellitus. In this study, a large series of 105 FBPase inhibitors were studied using a combinational method by 3D-QSAR, molecular docking and molecular dynamics simulations for a further improvement in potency. The optimal 3D models exhibit high statistical significance of the results, especially for the CoMFA results with r(ncv) (2), q(2) values of 0.986, 0.514 for internal validation, and r(pred) (2), r(m) (2) statistics of 0.902, 0.828 statistics for external validation. Graphic representation of the results, as contoured 3D coefficient plots, also provides a clue to the reasonable modification of molecules. (1) Substituents with a proper length and size at the C5 position of the thiazole core are required to enhance the potency; (2) A small and electron-withdrawing group at the C2 position linked to the thiazole core is likely to help increase the FBPase inhibition; (3) Substituent groups as hydrogen bond acceptors at the C2 position of the furan ring are favored. In addition, the agreement between 3D-QSAR, molecular docking and molecular dynamics simulation proves the rationality of the developed models. These results, we hope, may be helpful in designing novel and potential FBPase inhibitors.     

Combined 3D-QSAR and Docking Modelling Study on Indolocarbazole Series Compounds as Tie-2 Inhibitors

Tie-2, a kind of endothelial cell tyrosine kinase receptor, is required for embryonic blood vessel development and tumor angiogenesis. Several compounds that showed potent activity toward this attractive anticancer drug target in the assay have been reported. In order to investigate the structure-activity correlation of indolocarbazole series compounds and modify them to improve their selectivity and activity, 3D-QSAR models were built using CoMFA and CoMSIA methods and molecular docking was used to check the results. Based on the common sketch align, two good QSAR models with high predictabilities (CoMFA model: q(2) = 0.823, r(2) = 0.979; CoMSIA model: q(2) = 0.804, r(2) = 0.967) were obtained and the contour maps obtained from both models were applied to identify the influence on the biological activity. Molecular docking was then used to confirm the results. Combined with the molecular docking results, the detail binding mode between the ligands and Tie-2 was elucidated, which enabled us to interpret the structure-activity relationship. These satisf actory results not only offered help to comprehend the action mechanism of indolocarbazole series compounds, but also provide new information for the design of new potent inhibitors.     

Molecular Modeling Studies of 4,5-Dihydro-1H-pyrazolo[4,3-h] quinazoline Derivatives as Potent CDK2/Cyclin A Inhibitors Using 3D-QSAR and Docking

CDK2/cyclin A has appeared as an attractive drug targets over the years with diverse therapeutic potentials. A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) followed by molecular docking studies were performed on a series of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as potent CDK2/cyclin A inhibitors. The CoMFA and CoMSIA models, using 38 molecules in the training set, gave r(2) (cv) values of 0.747 and 0.518 and r(2) values of 0.970 and 0.934, respectively. 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained from molecular modeling studies may be helpful to design novel inhibitors of CDK2/cyclin A with desired activity.     

Pharmacophore and Molecular Docking Guided 3D-QSAR Study of Bacterial Enoyl-ACP Reductase (FabI) Inhibitors

Enoyl acyl carrier protein (ACP) reductase (FabI) is a potential target for the development of antibacterial agents. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for substituted formamides series of FabI inhibitors were investigated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Pharmacophore and molecular docking methods were used for construction of the molecular alignments. A training set of 36 compounds was performed to create the 3D-QSAR models and their external predictivity was proven using a test set of 11 compounds. Graphical interpretation of the results revealed important structural features of the formamides related to the active site of FabI. The results may be exploited for further optimization of the design of new potent FabI inhibitors.     

Structural Determination of Three Different Series of Compounds as Hsp90 Inhibitors Using 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Methods

Hsp90 is involved in correcting, folding, maturation and activation of a diverse array of client proteins; it has also been implicated in the treatment of cancer in recent years. In this work, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular docking and molecular dynamics were performed on three different series of Hsp90 inhibitors to build 3D-QSAR models, which were based on the ligand-based or receptor-based methods. The optimum 3D-QSAR models exhibited reasonable statistical characteristics with averaging internal q(2) > 0.60 and external r(2) (pred) > 0.66 for Benzamide tetrahydro-4H-carbazol-4-one analogs (BT), AT13387 derivatives (AT) and Dihydroxylphenyl amides (DA). The results revealed that steric effects contributed the most to the BT model, whereas H-bonding was more important to AT, and electrostatic, hydrophobic, H-bond donor almost contributed equally to the DA model. The docking analysis showed that Asp93, Tyr139 and Thr184 in Hsp90 are important for the three series of inhibitors. Molecular dynamics simulation (MD) further indicated that the conformation derived from docking is basically consistent with the average structure extracted from MD simulation. These results not only lead to a better understanding of interactions between these inhibitors and Hsp90 receptor but also provide useful information for the design of new inhibitors with a specific activity.     

Combined 3D-QSAR Modeling and Molecular Docking Studies on Pyrrole-Indolin-2-ones as Aurora A Kinase Inhibitors

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r(2) (cv) values of 0.726 and 0.566, and r(2) values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed.     

3D-QSAR and Molecular Docking Studies on Derivatives of MK-0457, GSK1070916 and SNS-314 as Inhibitors against Aurora B Kinase

Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q(2) = 0.605, r(2) (pred) = 0.826), (q(2) = 0.52, r(2) (pred) = 0.798) and (q(2) = 0.582, r(2) (pred) = 0.971) for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors.     

Investigation of the Structure Requirement for 5-HT(6) Binding Affinity of Arylsulfonyl Derivatives: A Computational Study

5-HT(6) receptor has been implicated in a series of diseases including anxiety, depression, schizophrenia and cognitive dysfunctions. 5-HT(6) ligands have been reported to play a significant role in the treatment for central nervous system (CNS) diseases. Presently, a large series of 223 5-HT(6) ligands were studied using a combinational method by 3D-QSAR, molecular docking and molecular dynamics calculations for further improvement of potency. The optimal 3D models exhibit satisfying statistical results with r(2) (ncv), q(2) values of 0.85 and 0.50 for CoMFA, 0.81 and 0.53 for CoMSIA, respectively. Their predictive powers were validated by external test set, showing r(2) (pred) of 0.71 and 0.76. The contour maps also provide a visual representation of contributions of steric, electrostatic, hydrophobic and hydrogen bond fields as well as the prospective binding models. In addition, the agreement between 3D-QSAR, molecular docking and molecular dynamics simulation proves the rationality of the developed models. These results, we hope, may be helpful in designing novel and potential 5-HT(6) ligands.     

Generation of Non-Nucleotide CD73 Inhibitors Using a Molecular Docking and 3D-QSAR Approach

Radiotherapy and chemotherapy are conventional cancer treatments. Around 60% of all patients who are diagnosed with cancer receive radio- or chemotherapy in combination with surgery during their disease. Only a few patients respond to the blockage of immune checkpoints alone, or in combination therapy, because their tumours might not be immunogenic. Under these circumstances, an increasing level of extracellular adenosine via the activation of ecto-5’-nucleotidase (CD73) and consequent adenosine receptor signalling is a typical mechanism that tumours use to evade immune surveillance. CD73 is responsible for the conversion of adenosine monophosphate to adenosine. CD73 is overexpressed in various tumour types. Hence, targetting CD73’s signalling is important for the reversal of adenosine-facilitated immune suppression. In this study, we selected a potent series of the non-nucleotide small molecule inhibitors of CD73. Molecular docking studies were performed in order to examine the binding mode of the inhibitors inside the active site of CD73 and 3D-QSAR was used to study the structure–activity relationship. The obtained CoMFA (q² = 0.844, ONC = 5, r² = 0.947) and CoMSIA (q² = 0.804, ONC = 4, r² = 0.954) models showed reasonable statistical values. The 3D-QSAR contour map analysis revealed useful structural characteristics that were needed to modify non-nucleotide small molecule inhibitors. We used the structural information from the overall docking and 3D-QSAR results to design new, potent CD73 non-nucleotide inhibitors. The newly designed CD73 inhibitors exhibited higher activity (predicted pIC₅₀) than the most active compound of all of the derivatives that were selected for this study. Further experimental studies are needed in order to validate the new CD73 inhibitors.     

3D-QSAR Studies on Barbituric Acid Derivatives as Urease Inhibitors and the Effect of Charges on the Quality of a Model

Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, 3D-QSAR (three dimensional quantitative structural activity relationship) advance studies were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. Different partial charges were calculated to examine their consequences on the predictive ability of the developed models. The finest developed model for CoMFA and CoMSIA were achieved by using MMFF94 charges. The developed CoMFA model gives significant results with cross-validation (q²) value of 0.597 and correlation coefficients (r²) of 0.897. Moreover, five different fields i.e., steric, electrostatic, and hydrophobic, H-bond acceptor and H-bond donors were used to produce a CoMSIA model, with q² and r² of 0.602 and 0.98, respectively. The generated models were further validated by using an external test set. Both models display good predictive power with r²_pred ≥ 0.8. The analysis of obtained CoMFA and CoMSIA contour maps provided detailed insight for the promising modification of the barbituric acid derivatives with an enhanced biological activity.     

Docking-Based 3D-QSAR Studies for 1,3,4-oxadiazol-2-one Derivatives as FAAH Inhibitors

This work aimed to construct 3D-QSAR CoMFA and CoMSIA models for a series of 31 FAAH inhibitors, containing the 1,3,4-oxadiazol-2-one moiety. The obtained models were characterized by good statistical parameters: CoMFA Q² = 0.61, R² = 0.98; CoMSIA Q² = 0.64, R² = 0.93. The CoMFA model field contributions were 54.1% and 45.9% for steric and electrostatic fields, respectively. In the CoMSIA model, electrostatic, steric, hydrogen bond donor, and hydrogen acceptor properties were equal to 34.6%, 23.9%, 23.4%, and 18.0%, respectively. These models were validated by applying the leave-one-out technique, the seven-element test set (CoMFA r²_test-set = 0.91; CoMSIA r²_test-set = 0.91), a progressive scrambling test, and external validation criteria developed by Golbraikh and Tropsha (CoMFA r²₀ = 0.98, k = 0.95; CoMSIA r²₀ = 0.98, k = 0.89). As the statistical significance of the obtained model was confirmed, the results of the CoMFA and CoMSIA field calculation were mapped onto the enzyme binding site. It gave us the opportunity to discuss the structure–activity relationship based on the ligand–enzyme interactions. In particular, examination of the electrostatic properties of the established CoMFA model revealed fields that correspond to the regions where electropositive substituents are not desired, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one moiety. This highlights the importance of heterocycle, a highly electronegative moiety in this area of each ligand. Examination of hydrogen bond donor and acceptor properties contour maps revealed several spots where the implementation of another hydrogen-bond-donating moiety will positively impact molecules’ binding affinity, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one ring. On the other hand, there is a large isopleth that refers to the favorable H-bond properties close to the terminal phenoxy group of a ligand, which means that, generally speaking, H-bond acceptors are desired in this area.     

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a −1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.     

Investigation on Quantitative Structure Activity Relationships and Pharmacophore Modeling of a Series of mGluR2 Antagonists

MGluR2 is G protein-coupled receptor that is targeted for diseases like anxiety, depression, Parkinson's disease and schizophrenia. Herein, we report the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of a series of 1,3-dihydrobenzo[ b][1,4]diazepin-2-one derivatives as mGluR2 antagonists. Two series of models using two different activities of the antagonists against rat mGluR2, which has been shown to be very similar to the human mGluR2, (activity I: inhibition of [(3)H]-LY354740; activity II: mGluR2 (1S,3R)-ACPD inhibition of forskolin stimulated cAMP.) were derived from datasets composed of 137 and 69 molecules respectively. For activity I study, the best predictive model obtained from CoMFA analysis yielded a Q(2) of 0.513, R(2) (ncv) of 0.868, R(2) (pred) = 0.876, while the CoMSIA model yielded a Q(2) of 0.450, R(2) (ncv) = 0.899, R(2) (pred) = 0.735. For activity II study, CoMFA model yielded statistics of Q(2) = 0.5, R(2) (ncv) = 0.715, R(2) (pred) = 0.723. These results prove the high predictability of the models. Furthermore, a combined analysis between the CoMFA, CoMSIA contour maps shows that: (1) Bulky substituents in R(7), R(3) and position A benefit activity I of the antagonists, but decrease it when projected in R(8) and position B; (2) Hydrophilic groups at position A and B increase both antagonistic activity I and II; (3) Electrostatic field plays an essential rule in the variance of activity II. In search for more potent mGluR2 antagonists, two pharmacophore models were developed separately for the two activities. The first model reveals six pharmacophoric features, namely an aromatic center, two hydrophobic centers, an H-donor atom, an H-acceptor atom and an H-donor site. The second model shares all features of the first one and has an additional acceptor site, a positive N and an aromatic center. These models can be used as guidance for the development of new mGluR2 antagonists of high activity and selectivity. This work is the first report on 3D-QSAR modeling of these mGluR2 antagonists. All the conclusions may lead to a better understanding of the mechanism of antagonism and be helpful in the design of new potent mGluR2 antagonists.     

Combined 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Derivatives of Peptide Epoxyketone and Tyropeptin-Boronic Acid as Inhibitors Against the β5 Subunit of Human 20S Proteasome

An abnormal ubiquitin-proteasome is found in many human diseases, especially in cancer, and has received extensive attention as a promising therapeutic target in recent years. In this work, several in silico models have been built with two classes of proteasome inhibitors (PIs) by using 3D-QSAR, homology modeling, molecular docking and molecular dynamics (MD) simulations. The study resulted in two types of satisfactory 3D-QSAR models, i.e., the CoMFA model (Q(2) = 0.462, R(2) (pred) = 0.820) for epoxyketone inhibitors (EPK) and the CoMSIA model (Q(2) = 0.622, R(2) (pred) = 0.821) for tyropeptin-boronic acid derivatives (TBA). From the contour maps, some key structural factors responsible for the activity of these two series of PIs are revealed. For EPK inhibitors, the N-cap part should have higher electropositivity; a large substituent such as a benzene ring is favored at the C6-position. In terms of TBA inhibitors, hydrophobic substituents with a larger size anisole group are preferential at the C8-position; higher electropositive substituents like a naphthalene group at the C3-position can enhance the activity of the drug by providing hydrogen bond interaction with the protein target. Molecular docking disclosed that residues Thr60, Thr80, Gly106 and Ser189 play a pivotal role in maintaining the drug-target interactions, which are consistent with the contour maps. MD simulations further indicated that the binding modes of each conformation derived from docking is stable and in accord with the corresponding structure extracted from MD simulation overall. These results can offer useful theoretical references for designing more potent PIs.     

Synthesis of Dihydrobenzofuro[3,2-b]chromenes as Potential 3CLpro Inhibitors of SARS-CoV-2: A Molecular Docking and Molecular Dynamics Study

The recent emergence of pandemic of coronavirus (COVID-19) caused by SARS-CoV-2 has raised significant global health concerns. More importantly, there is no specific therapeutics currently available to combat against this deadly infection. The enzyme 3-chymotrypsin-like cysteine protease (3CLpro) is known to be essential for viral life cycle as it controls the coronavirus replication. 3CLpro could be a potential drug target as established before in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). In the current study, we wanted to explore the potential of fused flavonoids as 3CLpro inhibitors. Fused flavonoids (5a,10a-dihydro-11H-benzofuro[3,2-b]chromene) are unexplored for their potential bioactivities due to their low natural occurrences. Their synthetic congeners are also rare due to unavailability of general synthetic methodology. Here we designed a simple strategy to synthesize 5a,10a-dihydro-11H-benzofuro[3,2-b]chromene skeleton and it's four novel derivatives. Our structural bioinformatics study clearly shows excellent potential of the synthesized compounds in comparison to experimentally validated inhibitor N3. Moreover, in-silico ADMET study displays excellent druggability and extremely low level of toxicity of the synthesized molecules. Further, for better understanding, the molecular dynamic approach was implemented to study the change in dynamicity after the compounds bind to the protein. A detailed investigation through clustering analysis and distance calculation gave us sound comprehensive data about their molecular interaction. In summary, we anticipate that the currently synthesized molecules could not only be a potential set of inhibitors against 3CLpro but also the insights acquired from the current study would be instrumental in further developing novel natural flavonoid based anti-COVID therapeutic spectrums.     

Discovery of Potent Vascular Endothelial Growth Factor Receptor‐2 Inhibitors

Substantial evidence over the last decades has implicated uncontrolled angiogenesis with various pathological states, including cancer. Vascular endothelial growth factor (VEGF) plays a critical role in its regulation. Because the tyrosine kinase VEGF receptor‐2 (VEGFR‐2) is the major mediator of the mitogenic, angiogenic, and permeability‐enhancing effects of VEGF, it has become one of the most profound anti‐angiogenesis targets. Inspired by the anthranilamide class of VEGFR‐2 inhibitors, we performed a computational analysis of some potent representative members, using docking and molecular dynamics calculations. Based on the observations drawn from introducing the effect of the receptor's flexibility in implicit aqueous environment, we designed, synthesized, and characterized several new analogues of related scaffolds with modifications in their steric and electronic characteristics. In vitro evaluation of these compounds revealed several novel VEGFR‐2 inhibitors that are less cytotoxic and more potent than the parent compounds.     

Studies of New Fused Benzazepine as Selective Dopamine D3 Receptor Antagonists Using 3D-QSAR, Molecular Docking and Molecular Dynamics

In recent years, great interest has been paid to the development of compounds with high selectivity for central dopamine (DA) D3 receptors, an interesting therapeutic target in the treatment of different neurological disorders. In the present work, based on a dataset of 110 collected benzazepine (BAZ) DA D3 antagonists with diverse kinds of structures, a variety of in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), homology modeling, molecular docking and molecular dynamics (MD) were carried out to reveal the requisite 3D structural features for activity. Our results show that both the receptor-based (Q(2) = 0.603, R(2) (ncv) = 0.829, R(2) (pre) = 0.690, SEE = 0.316, SEP = 0.406) and ligand-based 3D-QSAR models (Q(2) = 0.506, R(2) (ncv) =0.838, R(2) (pre) = 0.794, SEE = 0.316, SEP = 0.296) are reliable with proper predictive capacity. In addition, a combined analysis between the CoMFA, CoMSIA contour maps and MD results with a homology DA receptor model shows that: (1) ring-A, position-2 and R(3) substituent in ring-D are crucial in the design of antagonists with higher activity; (2) more bulky R(1) substituents (at position-2 of ring-A) of antagonists may well fit in the binding pocket; (3) hydrophobicity represented by MlogP is important for building satisfactory QSAR models; (4) key amino acids of the binding pocket are CYS101, ILE105, LEU106, VAL151, PHE175, PHE184, PRO254 and ALA251. To our best knowledge, this work is the first report on 3D-QSAR modeling of the new fused BAZs as DA D3 antagonists. These results might provide information for a better understanding of the mechanism of antagonism and thus be helpful in designing new potent DA D3 antagonists.     

Exploration of N-Arylsulfonyl-indole-2-carboxamide Derivatives as Novel Fructose-1,6-bisphosphatase Inhibitors by Molecular Simulation

A series of N-arylsulfonyl-indole-2-carboxamide derivatives have been identified as potent fructose-1,6-bisphosphatase (FBPase) inhibitors (FBPIs) with excellent selectivity for the potential therapy of type II diabetes mellitus. To explore the structure–activity relationships (SARs) and the mechanisms of action of these FBPIs, a systematic computational study was performed in the present study, including three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, pharmacophore modeling, molecular dynamics (MD), and virtual screening. The constructed 3D-QSAR models exhibited good predictive ability with reasonable parameters using comparative molecular field analysis (q² = 0.709, R² = 0.979, r_pre² = 0.932) and comparative molecular similarity indices analysis (q² = 0.716, R² = 0.978, r_pre² = 0.890). Twelve hit compounds were obtained by virtual screening using the best pharmacophore model in combination with molecular dockings. Three compounds with relatively higher docking scores and better ADME properties were then selected for further studies by docking and MD analyses. The docking results revealed that the amino acid residues Met18, Gly21, Gly26, Leu30, and Thr31 at the binding site were of great importance for the effective bindings of these FBPIs. The MD results indicated that the screened compounds VS01 and VS02 could bind with FBPase stably as its cognate ligand in dynamic conditions. This work identified several potential FBPIs by modeling studies and might provide important insights into developing novel FBPIs.