3D QSAR and molecular docking studies of benzimidazole derivatives as hepatitis C virus NS5B polymerase inhibitors

AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin

BACKGROUND

Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS).

OBJECTIVE

In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials.

METHODS

To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA.

RESULTS

Maps that were generated were used to determine the screen's r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors.

CONCLUSION

With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model's robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.

Exploration of 2D and 3D-QSAR analysis and docking studies for novel dihydropteridone derivatives as promising therapeutic agents targeting glioblastoma

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.

3D- and 2D-QSAR models’ study and molecular docking of novel nitrogen-mustard compounds for osteosarcoma

Background: The dipeptide-alkylated nitrogen-mustard compound is a new kind of nitrogen-mustard derivative with a strong anti-tumor activity, which can be used as a potential anti-osteosarcoma chemotherapy drug.

Objective: 2D- and 3D-QSAR (structure–activity relationship quantification) models were established to predict the anti-tumor activity of dipeptide-alkylated nitrogen-mustard compounds.

Method: In this study, a linear model was established using a heuristic method (HM) and a non-linear model was established using the gene expression programming (GEP) algorithm, but there were more limitations in the 2D model, so a 3D-QSAR model was introduced and established through the CoMSIA method. Finally, a series of new dipeptide-alkylated nitrogen-mustard compounds were redesigned using the 3D-QSAR model; docking experiments were carried out on several compounds with the highest activity against tumors.

Result: The 2D- and 3D-QSAR models obtained in this experiment were satisfactory. A linear model with six descriptors was obtained in this experiment using the HM through CODESSA software, where the descriptor “Min electroph react index for a C atom” has the greatest effect on the compound activity; a reliable non-linear model was obtained using the GEP algorithm model (the best model was generated in the 89th generation cycle, with a correlation coefficient of 0.95 and 0.87 for the training and test set, respectively, and a mean error of 0.02 and 0.06, respectively). Finally, 200 new compounds were designed by combining the contour plots of the CoMSIA model with each other, together with the descriptors in the 2D-QSAR, among which compound I1.10 had a high anti-tumor and docking ability.

Conclusion: Through the model established in this study, the factors influencing the anti-tumor activity of dipeptide-alkylated nitrogen-thaliana compounds were revealed, providing direction and guidance for the further design of efficient chemotherapy drugs against osteosarcoma.

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children's eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed. Result: The best linear model created using HM had an R², S², and R²cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q² (0.503), R² (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model's reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability. Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma

Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds. Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models. Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4). Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q² (0.63) and R ² (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability. Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.

Novel Elongator Protein 2 Inhibitors Mitigating Tumor Necrosis Factor-α Induced Osteogenic Differentiation Inhibition

Tumor necrosis factor-α is a common cytokine that increases in inflammatory processes, slows the differentiation of bone formation, and induces osteodystrophy in the long-term inflammatory microenvironment. Our previous study confirmed that the Elongation protein 2 (ELP2) plays a significant role in osteogenesis and osteogenic differentiation, which is considered a drug discovery target in diseases related to bone formation and differentiation. In this study, we applied an in silico virtual screening method to select molecules that bind to the ELP2 protein from a chemical drug molecule library and obtained 95 candidates. Then, we included 11 candidates by observing the docking patterns and the noncovalent bonds. The binding affinity of the ELP2 protein with the candidate compounds was examined by SPR analysis, and 5 out of 11 compounds performed good binding affinity to the mouse ELP2 protein. After in vitro cell differentiation assay, candidates 2# and 5# were shown to reduce differentiation inhibition after tumor necrosis factor-α stimulation, allowing further optimization and development for potential clinical treatment of inflammation-mediated orthopedic diseases.

Ruthenium Complexes with 2-Pyridin-2-yl-1H-benzimidazole as Potential Antimicrobial Agents: Correlation between Chemical Properties and Anti-Biofilm Effects

Antimicrobial resistance is a growing public health concern that requires urgent action. Biofilm-associated resistance to antimicrobials begins at the attachment phase and increases as the biofilms maturate. Hence, interrupting the initial binding process of bacteria to surfaces is essential to effectively prevent biofilm-associated problems. Herein, we have evaluated the antibacterial and anti-biofilm activities of three ruthenium complexes in different oxidation states with 2-pyridin-2-yl-1H-benzimidazole (L1 = 2,2'-PyBIm): [(η6-p-cymene)RuIIClL1]PF6 (Ru(II) complex), mer-[RuIIICl3(CH3CN)L1]·L1·3H2O (Ru(III) complex), (H2L1)2[RuIIICl4(CH3CN)2]2[RuIVCl4(CH3CN)2]·2Cl·6H2O (Ru(III/IV) complex). The biological activity of the compounds was screened against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa strains. The results indicated that the anti-biofilm activity of the Ru complexes at concentration of 1 mM was better than that of the ligand alone against the P. aeruginosa PAO1. It means that ligand, in combination with ruthenium ion, shows a synergistic effect. The effect of the Ru complexes on cell surface properties was determined by the contact angle and zeta potential values. The electric and physical properties of the microbial surface are useful tools for the examined aggregation phenomenon and disruption of the adhesion. Considering that intermolecular interactions are important and largely define the functions of compounds, we examined interactions in the crystals of the Ru complexes using the Hirshfeld surface analysis.

3D-quantitative structure-activity relationship and antiviral effects of curcumin derivatives as potent inhibitors of influenza H1N1 neuraminidase

Curcumin derivatives have been shown to inhibit replication of human influenza A viruses (IAVs). However, it is not clear whether curcumin and its derivatives can inhibit neuraminidase (NA) of influenza virus. In this study, a meaningful 3D quantitative structure–activity relationship model (comparative molecular field analysis R² = 0.997, q² = 0.527, s = 0.064, F = 282.663) was built to understand the chemical–biological interactions between their activities and neuraminidase. Molecular docking was used to predict binding models between curcumin derivatives and neuraminidase. Real-time polymerase chain reactions showed that the five active curcumin derivatives might have direct effects on viral particle infectivity in H1N1-infected lung epithelial (MDCK) cells. Neuraminidase activation assay showed that five active curcumin derivatives decreased H1N1-induced neuraminidase activation in MDCK cells. Indirect immunofluorescence assay indicated that two active curcumin derivatives (tetramethylcurcumin and curcumin) down-regulated the nucleoprotein expression. Curcumin inhibited IAV in vivo. The therapeutic mechanism of curcumin in the treatment of influenza viral pneumonia is related to improving the immune function of infected mice and regulating secretion of tumor necrosis-α, interleukin-6, and interferon-γ. These results indicate that curcumin derivatives inhibit IAV by blocking neuraminidase in the cellular model and curcumin also has anti-IAV activity in the animal model.

In Silico Studies against Viral Sexually Transmitted Diseases

Sexually Transmitted Diseases (STDs) refer to a variety of clinical syndromes and infections caused by pathogens that can be acquired and transmitted through sexual activity. Among STDs widely reported in the literature, viral sexual diseases have been increasing in a number of cases globally. This emphasizes the need for prevention and treatment. Among the methods widely used in drug planning are Computer-Aided Drug Design (CADD) studies and molecular docking which have the objective of investigating molecular interactions between two molecules to better understand the three -dimensional structural characteristics of the compounds. This review will discuss molecular docking studies applied to viral STDs, such as Ebola virus, Herpes virus and HIV, and reveal promising new drug candidates with high levels of specificity to their respective targets.

Single enzyme nanoparticle, an effective tool for enzyme replacement therapy

The term "single enzyme nanoparticle" (SEN) refers to a chemically or biologically engineered single enzyme molecule. SENs are distinguished from conventional protein nanoparticles in that they can maintain their individual structure and enzymatic activity following modification. Furthermore, SENs exhibit enhanced properties as biopharmaceuticals, such as reduced antigenicity, and increased stability and targetability, which are attributed to the introduction of specific moieties, such as poly(ethylene glycol), carbohydrates, and antibodies. Enzyme replacement therapy (ERT) is a crucial therapeutic option for controlling enzyme-deficiency-related disorders. However, the unfavorable properties of enzymes, including immunogenicity, lack of targetability, and instability, can undermine the clinical significance of ERT. As shown in the cases of Adagen®, Revcovi®, Palynziq®, and Strensiq®, SEN can be an effective technology for overcoming these obstacles. Based on these four licensed products, we expect that additional SENs will be introduced for ERT in the near future. In this article, we review the concepts and features of SENs, as well as their preparation methods. Additionally, we summarize different types of enzyme deficiency disorders and the corresponding therapeutic enzymes. Finally, we focus on the current status of SENs in ERT by reviewing FDA-approved products.

3D-QSAR, molecular docking, and molecular dynamics simulation of a novel thieno[3,4-d]pyrimidine inhibitor targeting human immunodeficiency virus type 1 reverse transcriptase

Human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) is one of the most attractive drug targets for the treatment of AIDS. In this study, 67 thieno[3,4-d]pyrimidine derivatives were selected as novel HIV-1 RT inhibitors to combat viral resistance, and were subjected to 3 D-QSAR studies using CoMFA, CoMSIA, and T-CoMFA. In the 3 D-QSAR study, two methods of ligand-based alignment and pharmacophore-based alignment were used. The results showed that CoMFA (n = 8; q² = 0.594; r² = 0.974) and CoMSIA (n = 7; q² = 0.528; r² = 0.965) have good stability and predictability. The molecular docking study showed that the hydrogen bonding and van der Waals interactions of key residues such as Leu100, Lys101, Val106, Phe227 and Pro236 play an important role in ligand-receptor binding. Based on these results, 12 new thieno[3,4-d]pyrimidines were designed and their activities were predicted; the results indicated that these compounds have good predictive activity and reasonably good ADME/T profiles. MD simulation analysis of 50 ns showed that compound 23j formed four hydrogen bonds with the residues (Lys101, Lys104, Val106 and Thr318), and binds more closely to HIV-1 RT than compound 23j. Furthermore, the group at the R₁ position and the horseshoe-like conformation of these compounds are critical for the inhibitory activity and stability. These results provide useful insights for the discovery and design of a new generation of HIV-1 RT inhibitors.Communicated by Ramaswamy H. Sarma.

The activity prediction of indole inhibitors against HCV NS5B polymerase

Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus. Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC₅₀ ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q² ) for the training set and the R-squared of prediction ( R p 2 ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique.

Combined 3D-QSAR, molecular docking and molecular dynamics study on the benzimidazole inhibitors targeting HCV NS5B polymerase

The hepatitis C virus (HCV)-infected population has continued to grow during recent years, and novel HCV antiviral agents are urgently needed. In this work, a combined theoretical study was performed on the HCV non-structural 5B (NS5B) polymerase and 53 benzimidazole inhibitors. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out with ligand-based and receptor-based alignments. Ligand-based QSAR models (cross-validated q² of 0.918 for CoMFA and 0.825 for CoMSIA) were found to be superior to receptor-based approaches (cross-validated q² of 0.765 for CoMFA and 0.740 for CoMSIA). Based on the most predictive CoMFA and CoMSIA models, the structural features that were essential for the inhibitory activity of benzimidazoles were characterized. A molecular dynamics study revealed that the induced fit effect between NS5B and its substrate may be responsible for the inferiority of the receptor-based CoMFA and CoMSIA models. The binding-free energy calculated using the MM/PBSA method correlated well with the experimental results and revealed that the van der Waals and electrostatic interactions most contributed to the binding. In addition, energetically favorable NS5B residues were identified by the per-residue decomposition of binding-free energy. The results presented in this work provide meaningful information for the design of novel benzimidazole inhibitors targeting the NS5B polymerase.Communicated by Ramaswamy H. Sarma.

Quantum molecular modeling of hepatitis C virus inhibition through non-structural protein 5B polymerase receptor binding of C5-arylidene rhodanines

We have carried out high-level quantum chemical computations followed by molecular docking studies on a set of 17C₅-arylidene rhodanine isomers to provide insights into the binding modes with different reported binding pockets of the nonstructural protein 5B (NS5B) polymerase that contribute to the hepatitis C virus (HCV) inhibition. We optimized the multi-target profile of the selected rhodanine analogs to investigate potential non-nucleotide inhibitors (NNIs) by quantum chemical optimization of the 18 isomers followed by docking with quantum chemically optimized structures of each isomer with NS5B polymerase at multiple binding pockets. The binding affinities of the PP-I, PP-II and TP-II pockets of NS5B polymerase were analyzed for all the 17 isomers of 2-[(5Z)-5-(2,4-dichlorobenzylidene)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]-3-phenylpropanoic acid. On the basis of binding propensity at the different pockets and inhibitor constants, we ranked these isomers as potential candidates for the HCV inhibition. We have identified four isomers as promising NNIs of NS5B polymerase with comparable binding and inhibition to the standard (1,3) dichloro substituted isomer that exhibits in vitro activity and several other isomers as candidates in a "multi-targeted drug" approach.

Structure-based drug design of novel peptidomimetic cellulose derivatives as HCV-NS3 protease inhibitors

Hepatitis C Virus (HCV) represents a global health threat not only due to the large number of reported worldwide HCV infections, but also due to the absence of a reliable vaccine for its prevention. HCV NS3 protease is one of the most important targets for drug design aiming at the deactivation of HCV. In the present work, molecular docking simulations are carried out for suggested novel NS3 protease inhibitors applied to the Egyptian genotype 4. These inhibitors are modifications of dimer cellulose by adding a hexa-peptide to the cellulose at one of the positions 2, 3, 6, 2', 3' or 6'. Results show that the inhibitor compound with the hexa-peptide at position 6 shows significantly higher simulation docking score with HCV NS3 protease active site. This is supported by low total energy value of docking system, formation of two H-bonds with HCV NS3 protease active site residues, high binding affinity and increased stability in the interaction system.

Synthesis, crystal structure and biological activities of a Ag(I) complex based on the V-shaped ligand 1,3-bis(1-benzylbenzimidazol-2-yl)-2-thiapropane

A new silver(I) complex with 1,3-bis(1-benzylbenzimidazol-2-yl)-2-thiapropane (bbbt) and crotonate ligands, with composition [Ag(bbbt)(crotonate)]·CH3CH2OH, has been synthesized and characterized by physicochemical and spectroscopic methods. Single-crystal X-ray diffraction revealed that the coordination environment of the silver(I) center can be described as a distorted tetrahedral geometry, the Ag(I) atom being coordinated by two oxygen atoms from a crotonate anion and two nitrogen atoms from a bbbt ligand. Experimental studies of the DNA-binding properties indicated that the free ligand and the silver(I) complex bind to DNA by intercalation, and the order of the binding affinity is silver(I) complex>free bbbt ligand. The Ag(I) complex exhibits scavenger properties for hydroxyl radicals in in vitro studies.

Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures

The development of quantitative structure–activity relationship (QSAR) methods is going very fast for the last decades. OSAR approach already plays an important role in lead structure optimization, and nowadays, with development of big data approaches and computer power, it can even handle a huge amount of data associated with combinatorial chemistry. One of the recent developments is a three-dimensional QSAR, i.e., 3D QSAR. For the last two decades, 3D-OSAR has already been successfully applied to many datasets, especially of enzyme and receptor ligands. Moreover, quite often 3D QSAR investigations are going together with protein–ligand docking studies and this combination works synergistically. In this review, we outline recent advances in development and applications of 3D QSAR and protein–ligand docking approaches, as well as combined approaches for conventional organic compounds and for nanostructured materials, such as fullerenes and carbon nanotubes.

Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors

Nonstructural 5B (NS5B) polymerase and Nonstructural 3/4A (NS3/4A) protease have proven to be promising targets for the development of anti-HCV (Hepatitis C Virus) agents. The NS5B polymerase is of paramount importance in HCV viral replication; therefore, employing NS5B inhibitors was considered an effective way for the treatment of HCV. Identifying inhibitors against NS3/4A serine protease represents another attractive approach applied in anti-HCV drug discovery, which is evidenced by its crucial role of in the biogenesis of the viral replication activity. In this chapter, many different computational approaches including Quantitative Structure-Activity Relationship (QSAR) and virtual screening in anti-HCV drug discovery were considered and discussed in detail. Virtual Screening (VS) techniques, including ligand-based and structure-based, and QSAR have been utilized for the discovery of NS5B inhibitors. Moreover, using various in silico protocols and workflows, a number of studies have been conducted with an aim of identifying potential NS3/4A blockage agents.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Discovery of Novel Hepatitis C Virus NS5B Polymerase Inhibitors by Combining Random Forest, Multiple e-Pharmacophore Modeling and Docking

The NS5B polymerase is one of the most attractive targets for developing new drugs to block Hepatitis C virus (HCV) infection. We describe the discovery of novel potent HCV NS5B polymerase inhibitors by employing a virtual screening (VS) approach, which is based on random forest (RB-VS), e-pharmacophore (PB-VS), and docking (DB-VS) methods. In the RB-VS stage, after feature selection, a model with 16 descriptors was used. In the PB-VS stage, six energy-based pharmacophore (e-pharmacophore) models from different crystal structures of the NS5B polymerase with ligands binding at the palm I, thumb I and thumb II regions were used. In the DB-VS stage, the Glide SP and XP docking protocols with default parameters were employed. In the virtual screening approach, the RB-VS, PB-VS and DB-VS methods were applied in increasing order of complexity to screen the InterBioScreen database. From the final hits, we selected 5 compounds for further anti-HCV activity and cellular cytotoxicity assay. All 5 compounds were found to inhibit NS5B polymerase with IC50 values of 2.01-23.84 μM and displayed anti-HCV activities with EC50 values ranging from 1.61 to 21.88 μM, and all compounds displayed no cellular cytotoxicity (CC50 > 100 μM) except compound N2, which displayed weak cytotoxicity with a CC50 value of 51.3 μM. The hit compound N2 had the best antiviral activity against HCV, with a selective index of 32.1. The 5 hit compounds with new scaffolds could potentially serve as NS5B polymerase inhibitors through further optimization and development.

Approach to thiazole-containing tetrahydropyridines via Aza–Rauhut–Currier reaction and their potent fungicidal and insecticidal activity

Insecticidal and fungicidal active thiazole-containing tetrahydropyridine derivatives with accurately predicted 3D QSAR model againstAphis LaburniKaltenbach and predicted potential anti-fungus target of fumarate reductase without cross resistance were synthesized.

Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations

Presently, 151 widely-diverse pyridinylimidazole-based compounds that show inhibitory activities at the TNF-α release were investigated. By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored. The proposed pharmacophore model, including two hydrophobic sites, two aromatic centers, two H-bond donor atoms, two H-bond acceptor atoms, and two H-bond donor sites characterizes the necessary structural features of TNF-α release inhibitors. Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q² (cross-validated correlation coefficient) = 0.557, R²_ncv (non-cross-validated correlation coefficient) = 0.740, R²_pre (predicted correlation coefficient) = 0.749 and Q² = 0.598, R²_ncv = 0.767, R²_pre = 0.860, respectively). Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions. The corresponding contour maps generated by these models provide more diverse information about the key intermolecular interactions of inhibitors with the surrounding environment. All these models have extended the understanding of imidazole-based compounds in the structure-activity relationship, and are useful for rational design and screening of novel 2-thioimidazole-based TNF-α release inhibitors.

Structure-activity relationship in a purine-scaffold compound series with selectivity for the endoplasmic reticulum Hsp90 paralog Grp94

Grp94 is involved in the regulation of a restricted number of proteins and represents a potential target in a host of diseases, including cancer, septic shock, autoimmune diseases, chronic inflammatory conditions, diabetes, coronary thrombosis, and stroke. We have recently identified a novel allosteric pocket located in the Grp94 N-terminal binding site that can be used to design ligands with a 2-log selectivity over the other Hsp90 paralogs. Here we perform extensive SAR investigations in this ligand series and rationalize the affinity and paralog selectivity of choice derivatives by molecular modeling. We then use this to design 18c, a derivative with good potency for Grp94 (IC50 = 0.22 μM) and selectivity over other paralogs (>100- and 33-fold for Hsp90α/β and Trap-1, respectively). The paralog selectivity and target-mediated activity of 18c was confirmed in cells through several functional readouts. Compound 18c was also inert when tested against a large panel of kinases. We show that 18c has biological activity in several cellular models of inflammation and cancer and also present here for the first time the in vivo profile of a Grp94 inhibitor.

Structure determination of three furan-substituted benzimidazoles and calculation of π-π and C-H···π interaction energies

The synthesis and structural characterization of 2-(furan-2-yl)-1-(furan-2-ylmethyl)-1H-benzimidazole [C16H12N2O2, (I)], 2-(furan-2-yl)-1-(furan-2-ylmethyl)-1H-benzimidazol-3-ium chloride monohydrate [C16H13N2O2(+)·Cl(-)·H2O, (II)] and the hydrobromide salt 5,6-dimethyl-2-(furan-2-yl)-1-(furan-2-ylmethyl)-1H-benzimidazol-3-ium bromide [C18H17N2O2(+)·Br(-), (III)] are described. Benzimidazole (I) displays two sets of aromatic interactions, each of which involves pairs of molecules in a head-to-tail arrangement. The first, denoted set (Ia), exhibits both intermolecular C-H···π interactions between the 2-(furan-2-yl) (abbreviated as Fn) and 1-(furan-2-ylmethyl) (abbreviated as MeFn) substituents, and π-π interactions involving the Fn substituents between inversion-center-related molecules. The second, denoted set (Ib), involves π-π interactions involving both the benzene ring (Bz) and the imidazole ring (Im) of benzimidazole. Hydrated salt (II) exhibits N-H···OH2···Cl hydrogen bonding that results in chains of molecules parallel to the a axis. There is also a head-to-head aromatic stacking of the protonated benzimidazole cations in which the Bz and Im rings of one molecule interact with the Im and Fn rings of adjacent molecules in the chain. Salt (III) displays N-H···Br hydrogen bonding and π-π interactions involving inversion-center-related benzimidazole rings in a head-to-tail arrangement. In all of the π-π interactions observed, the interacting moieties are shifted with respect to each other along the major molecular axis. Basis set superposition energy-corrected (counterpoise method) interaction energies were calculated for each interaction [DFT, M06-2X/6-31+G(d)] employing atomic coordinates obtained in the crystallographic analyses for heavy atoms and optimized H-atom coordinates. The calculated interaction energies are -43.0, -39.8, -48.5, and -55.0 kJ mol(-1) for (Ia), (Ib), (II), and (III), respectively. For (Ia), the analysis was used to partition the interaction energies into the C-H···π and π-π components, which are 9.4 and 24.1 kJ mol(-1), respectively. Energy-minimized structures were used to determine the optimal interplanar spacing, the slip distance along the major molecular axis, and the slip distance along the minor molecular axis for 2-(furan-2-yl)-1H-benzimidazole.

In vitro antiviral effects and 3D QSAR study of resveratrol derivatives as potent inhibitors of influenza H1N1 neuraminidase

The anti-influenza virus activities of 50 resveratrol (RV: 3, 5, 4'-trihydroxy-trans-stilbene) derivatives were evaluated using a neuraminidase (NA) activity assay. The results showed that 35 compounds exerted an inhibitory effect on the NA activity of the influenza virus strain A/PR/8/34 (H1N1) with 50% inhibitory concentration (IC50) values ranging from 3.56 to 186.1 μm. Next, the 35 RV derivatives were used to develop 3D quantitative structure-activity relationship (3D QSAR) models for understanding the chemical-biological interactions governing their activities against NA. The comparative molecular field analysis (CoMFA r2=0.973, q2=0.620, qtest2=0.661) and the comparative molecular similarity indices analysis (CoMSIA r2=0.956, q2=0.610, qtest2=0.531) were applied. Afterward, molecular docking was performed to study the molecular interactions between the RV derivatives and NA. Finally, a cytopathic effect (CPE) reduction assay was used to evaluate the antiviral effects of the RV derivatives in vitro. Time-of-addition studies demonstrated that the RV derivatives might have a direct effect on viral particle infectivity. Our results indicate that the RV derivatives are potentially useful antiviral compounds for new drug design and development for influenza treatment.

Structural features of falcipain-3 inhibitors: an in silico study

Falcipain-3, the major cysteine hemoglobinase from the human malaria parasite Plasmodium falciparum, is critical for parasite development and is considered as a promising chemotherapeutic target. In order to understand the structure-activity correlation of falcipain-3 inhibitors, a set of ligand- and receptor-based 3D-QSAR models were developed in the present work employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for 247 2-pyrimidinecarbonitrile derivatives. An optimum ligand-based CoMSIA model yielded a cross validation Q(2) = 0.501, non-cross validation Rncv(2) = 0.821 and predictive Rpred(2) = 0.750. In addition, docking analysis and molecular dynamics simulation were applied to elucidate the probable binding modes of the ligand in the falcipain-3 binding pocket. Graphic representation of the results, as contoured 3D coefficient plots, also provides a clue to the reasonable modification of molecules. (1) Bulky substituents at the 3-position, and rings B and D increase the biological activity; (2) electrostatic groups at rings B, C and D are likely helpful to increase the falcipain-3 inhibition; (3) hydrophobic groups at rings B and D are favored; (4) Gly92, Ile94 and Thr95 which formed several H-bonds and a water-bridged H-bond are crucial for falcipain-3 inhibitors. This model, we hope, will be of help in designing and predicting novel falcipain-3 inhibitors.

Quantitative structure and bioactivity relationship study on HCV NS5B polymerase inhibitors

Several QSAR (quantitative structure-activity relationship) models for predicting the inhibitory activity of 333 hepatitis C virus (HCV) NS5B polymerase inhibitors were developed. All the inhibitors are HCV polymerase non-nucleoside analogue inhibitors (NNIs) fitting into the pocket of the NNI III binding site. For each molecule, global descriptors and 2D property autocorrelation descriptors were calculated from the program ADRIANA.Code. Pearson correlation analysis was used to select the significant descriptors for building models. The whole dataset was split into a training set and a test set randomly or using a Kohonen's self-organizing map (SOM). Then, the inhibitory activity of 333 HCV NS5B polymerase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) method, respectively. For the test set of the best model (Model 2B), correlation coefficient of 0.91 was achieved. Some molecular descriptors, such as molecular complexity (Complexity), the number of hydrogen bonding donors (HDon) and the solubility of the molecule in water (log S) were found to be very important factors which determined the bioactivity of the HCV NS5B inhibitors. Some other molecular properties such as electrostatic and charge properties also played important roles in the interaction between the ligand and the protein. The selected molecular descriptors were further confirmed by analysing the interaction between two representative inhibitors and the polymerase in their crystal structures.

Experimental and structural testing module to analyze paralogue-specificity and affinity in the Hsp90 inhibitors series

We here describe the first reported comprehensive analysis of Hsp90 paralogue affinity and selectivity in the clinical Hsp90 inhibitor chemotypes. This has been possible through the development of a versatile experimental assay based on a new FP-probe (16a) that we both describe here. The assay can test rapidly and accurately the binding affinity of all major Hsp90 chemotypes and has a testing range that spans low nanomolar to millimolar binding affinities. We couple this assay with a computational analysis that allows for rationalization of paralogue selectivity and defines not only the major binding modes that relay pan-paralogue binding or, conversely, paralogue selectivity, but also identifies molecular characteristics that impart such features. The methods developed here provide a blueprint for parsing out the contribution of the four Hsp90 paralogues to the perceived biological activity with the current Hsp90 chemotypes and set the ground for the development of paralogue selective inhibitors.

Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study

Background

HIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.

Results

In our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the L-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.

Conclusions

The framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.

Allosteric inhibition of the hepatitis C virus NS5B polymerase: in silico strategies for drug discovery and development

Chronic infection by hepatitis C virus (HCV) often leads to severe liver disease including cirrhosis, hepatocellular carcinoma and liver failure. Despite it being more than 20 years since the identification of HCV, the current standard of care for treating the infection is based on aspecific therapy often associated with severe side effects and low-sustained virological response. Research is ongoing to develop new and better medications, including a broad range of allosteric NS5B polymerase inhibitors. This article reviews traditional computational methodologies and more recently developed in silico strategies aimed at identifying and optimizing non-nucleoside inhibitors targeting allosteric sites of HCV NS5B polymerase. The drug-discovery approaches reviewed could provide take-home lessons for general computer-aided research projects.

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of some benzimidazole derivatives with trichomonicidal activity

Trichomonosis is a common sexually transmitted infectious disease linked to reproductive health complications. Recently, the benzimidazole nucleus has emerged as a promising scaffold to develop new trichomonicidal agents. Despite the fact that large amounts of experimental data have been accumulated over the past eight years, no quantitative studies have yet been reported on this class of compounds. In our effort to develop new antiparasitic benzimidazole derivatives, we report in this paper CoMFA and CoMSIA studies with an initial set of 70 benzimidazole derivatives with trichomonicidal activity. Four CoMFA models and eight CoMSIA models were generated; ten of these models had values of r(2) > 0.6 and q(2) > 0.5. The best CoMFA model had r(2) = 0.936 and q(2) = 0.634, and the best CoMSIA model had r(2) = 0.858 and q(2) = 0.642. These models were generated by using two conformer selection methodologies (minimum energy conformations and 3D similarity), and three charge types (Mulliken, Gasteiger-Hükel and electrostatic potential atomic charges). The putative active tautomers of 1H-benzimidazole derivatives were selected using 3D-QSAR calculations. All models were validated via an external test set with 13 molecules. The best models satisfied additional validation criteria. The contour maps generated show the most important features that a benzimidazole derivative should have for trichomonicidal activity; they also, suggest that substituents at the 2- and 6-positions are important in the generation of derivatives with strong activity.