3D-QSAR studies of azaoxoisoaporphine, oxoaporphine, and oxoisoaporphine derivatives as anti-AChE and anti-AD agents by the CoMFA method

3D-QSAR and ADMET studies of morpholino-pyrimidine inhibitors of DprE1 from Mycobacterium tuberculosis

DprE1 is involved in the synthesis of Mycobacterium tuberculosis cell wall and is a potent drug target for Tuberculosis (TB) treatment. The structure and dynamics of the loops L-I and L-II flanking the inhibitor binding site was studied using molecular dynamics (MD) simulation and MMPBSA in Amber v18. Docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) of 55 Morpholino-pyrimidine (MP) inhibitors was carried out using Autodock v1.2.0 and Forge v10. ADMET analysis was done using SwissADME and pkCSM. All MP inhibitors docked in the DprE1 binding pocket, making contacts with L-II residues. MD studies showed that L-I and L-II unfold in the absence of the inhibitor but fold stably structure with reduced protein motions in the presence of MP-38, the highest affinity inhibitor. This was confirmed by k-means clustering and secondary structure analysis. L-II residues, L317, F320 and R325 contributed most towards the MMPBSA binding free energy of MP-38. A robust field-based 3D-QSAR model showed values of r2train = 0.982, r2test = 0.702 and q2 = 0.516. The MP inhibitor field points were broadly divided into negative electrostatics near the A, B rings and hydrophobic electrostatics near the D, E rings. Addition of negative groups at methanone position and ring B as well as addition of hydrophobic and bulky groups at ring E will improve activity. Highly active compounds 47, 49 and 50 of MP series exhibited highly favourable drug-like properties. SAR and ADMET insights attained from this model will help in the development of active DprE1 inhibitors in future.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

Computational Studies on Acetylcholinesterase Inhibitors: From Biochemistry to Chemistry

Acetylcholinesterase inhibitors are the most promising therapeutics for Alzheimer's disease treatment as these prevent the loss of acetylcholine and slows the progression of the disease. The drugs approved for the management of Alzheimer's disease by the FDA are acetylcholinesterase inhibitors but are associated with side effects. Consistent and stringent efforts by the researchers with the help of computational methods opened new ways of developing novel molecules with good acetylcholinesterase inhibitory activity. In this manuscript, we reviewed the studies that identified the essential structural features of acetylcholinesterase inhibitors at the molecular level as well as the techniques like molecular docking, molecular dynamics, quantitative structure-activity relationship, virtual screening, and pharmacophore modelling that were used in designing these inhibitors.

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC₅₀ values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R² = 0.86 and Q² = 0.73), pls_38 (R² = 0.77 and Q² = 0.72), kpls_desc_44 (R² = 0.81 and Q² = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.

Docking and 3D-QSAR Studies of Hydrazone and Triazole Derivatives for Selective Inhibition of GRK2 over ROCK2

Introduction:

G protein-coupled receptor kinase 2 (GRK2) is known to be implicated in heart failure, and therefore serves as an important drug target. GRK2 belongs to the protein kinase A, G, and C family and shares high sequence similarity with its closely related protein, the Rhoassociated coiled-coil protein kinase 2 (ROCK2). Therefore, selective inhibition of GRK2 over ROCK2 is considered crucial for heart failure therapy.

Objective:

To understand the structural factors for enhancing the inhibitory activity for GRK2 and selectivity over ROCK2, we analyzed and compared molecular interactions using the same set of ligands against both receptors.

Methods:

We have performed molecular docking and three-dimensional quantitative structure activity relationship (3D-QSAR) studies on a series of hydrazone and triazole derivatives.

Results:

The presence of hydrophobic substituents at the triazole ring, electronegative substituents between the pyridine and triazole ring and hydrophobic substituents near the benzene ring increases the activity of both kinases. Whereas, having non-bulky substituents near the triazole ring, bulky and hydrophobic substations at the benzene ring and electronegative and H-bond acceptor substituents at the triazole ring showed a higher inhibitory preference for GRK2 over ROCK2.

Conclusion:

The outcome of this study may be used in the future development of potent GRK2 inhibitors having ROCK2 selectivity.

Three Dimensional Quantitative Structure Activity Relationship and Pharmacophore Modeling of Tacrine Derivatives as Acetylcholinesterase Inhibitors in Alzheimer's Treatment

Background:

Three dimensional quantitative structure activity relationship and pharmacophore modeling are studied for tacrine derivatives as acetylcholinesterase inhibitors.

Methods:

The three dimensional quantitative structure–activity relationship and pharmacophore methods were used to model the 68 derivatives of tacrine as human acetylcholinesterase inhibitors. The effect of the docked conformer of each molecule in the enzyme cavity was investigated on the predictive ability and statistical quality of the produced models.

Results:

The whole data set was divided into two training and test sets using hierarchical clustering method. 3D-QSAR model, based on the comparative molecular field analysis has good statistical parameters as indicated by q2 =0.613, r2 =0.876, and r2pred =0.75. In the case of comparative molecular similarity index analysis, q2, r2 and r2pred values were 0.807, 0.96, and 0.865 respectively. The statistical parameters of the models proved that the inhibition data are well fitted and they have satisfactory predictive abilities.

Conclusion :

The results from this study illustrate the reliability of using techniques in exploring the likely bonded conformations of the ligands in the active site of the protein target and improve the understanding over the structural and chemical features of AChE.

Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR, molecular docking and molecular dynamic simulations

Phosphopantetheine adenylyltransferase (PPAT) has been recognized as a promising target to develop novel antimicrobial agents, which is a hexameric enzyme that catalyzes the penultimate step in coenzyme A biosynthesis. In this work, molecular modeling study was performed with a series of PPAT inhibitors using molecular docking, three-dimensional qualitative structure-activity relationship (3D-QSAR) and molecular dynamic (MD) simulations to reveal the structural determinants for their bioactivities. Molecular docking study was applied to understand the binding mode of PPAT with its inhibitors. Subsequently, 3D-QSAR model was constructed to find the features required for different substituents on the scaffolds. For the best comparative molecular field analysis (CoMFA) model, the Q² and R² values of which were calculated as 0.702 and 0.989, while they were calculated as 0.767 and 0.983 for the best comparative molecular similarity index analysis model. The statistical data verified the significance and accuracy of our 3D-QSAR models. Furthermore, MD simulations were carried out to evaluate the stability of the receptor-ligand contacts in physiological conditions, and the results were consistent with molecular docking studies and 3D-QSAR contour map analysis. Binding free energy was calculated with molecular mechanics generalized born surface area approach, the result of which coincided well with bioactivities and demonstrated that van der Waals accounted for the largest portion. Overall, our study provided a valuable insight for further research work on the recognition of potent PPAT inhibitors.Communicated by Ramaswamy H. Sarma.

Methodologies Related to Computational Models in View of Developing Anti-Alzheimer Drugs: An Overview

BACKGROUND

Since the last two decades, there has been more focus on the development strategies related to Anti-Alzheimer's drug research. This may be attributed to the fact that most of the Alzheimer's cases are still mostly unknown except for a few cases, where genetic differences have been identified. With the progress of the disease, the symptoms involve intellectual deterioration, memory impairment, abnormal personality and behavioural patterns, confusion, aggression, mood swings, irritability Current therapies available for this disease give only symptomatic relief and do not focus on manipulations of biololecular processes.

METHODS

Nearly all the therapies to treat Alzheimer's disease, target to change the amyloid cascade which is considered to be important in AD pathogenesis. New drug regimens are not able to keep pace with the ever-increasing understanding about dementia at the molecular level. Looking into these aggravated problems, we thought to put forth molecular modeling as a drug discovery approach for developing novel drugs to treat Alzheimer disease. The disease is incurable and it gets worst as it advances and finally causes death. Due to this, the design of drugs to treat this disease has become an utmost priority for research. One of the most important emerging technologies applied for this has been Computer-assisted drug design (CADD). It is a research tool that employs large-scale computing strategies in an attempt to develop a model receptor site which can be used for designing of an anti-Alzheimer drug.

RESULTS

Various models of amyloid-based calcium channels have been computationally optimized. Docking and De novo evolution are used to design the compounds. They are further subjected to absorption, distribution, metabolism, excretion and toxicity (ADMET) studies to finally bring about active compounds that are able to cross BBB. Many novel compounds have been designed which might be promising ones for the treatment of AD.

CONCLUSION

The present review describes the research carried out on various heterocyclic scaffolds that can serve as lead compounds to design Anti-Alzheimer's drugs in the future. The molecular modeling methods can thus become a better alternative for the discovery of newer Anti- Alzheimer agents. This methodology is extremely useful to design drugs in minimum time with enhanced activity keeping balanced ethical considerations. Thus, the researchers are opting for this improved process over the conventional methods hoping to achieve a sure shot way out for the sufferings of people affected by Alzheimer besides other diseases.

Synthesis, insecticidal activities and structure-activity relationship study of dual chiral sulfilimines

To investigate the "methyl" impact on bioactivity of sulfiliminyl dicarboxamides, a total of 16 novel N-cyano and N-trifluoroacetyl sulfiliminyl dicarboxamides containing m-heptafluoroisopropylated aromatic amino moiety were studied. Two series of sulfiliminyl substituents were designed, synthesized and evaluated against oriental armyworm (Pseudaletia separata Walker) for their insecticidal activities. Their chemical structures were established by corresponding [Formula: see text] NMR, HRMS and optical polarimetry. Bioassay results revealed that some of the title compounds showed potent insecticidal activities against oriental armyworm. Notably, compounds IIa, IIIa, IVa exhibited 100% activity at [Formula: see text], in particular, IIa showed a comparable control efficacy to that of the commercial product flubendiamide. The SAR of these N-cyano sulfiliminyl isomers can be summarized as follows (Sc, Ss) [Formula: see text] (Sc, Rs), while the N-trifluoroacetyl sulfiliminyl isomers is (Sc, Rs) [Formula: see text] (Sc, Ss). Comparative molecular field analysis indicated that an electropositive substituent, [Formula: see text] group in the benzene ring was very important for the improvement in biological activity. These results could hold promise for novel chiral sulfiliminyl RyR regulators.

Receptor-guided 3D-QSAR studies, molecular dynamics simulation and free energy calculations of Btk kinase inhibitors

BACKGROUND

Bruton tyrosine kinase (Btk) plays an important role in B-cell development, differentiation, and signaling. It is also found be in involved in male immunodeficiency disease such as X-linked agammaglobulinemia (XLA). Btk is considered as a potential therapeutic target for treating autoimmune diseases and hematological malignancies.

RESULTS

In this work, a combined molecular modeling study was performed on a series of thieno [3,2-c] pyridine-4-amine derivatives as Btk inhibitors. Receptor-guided COMFA (q 2 = 0.574, NOC = 3, r 2 = 0.924) and COMSIA (q 2 = 0.646, NOC = 6, r 2 = 0.971) models were generated based on the docked conformation of the most active compound 26. All the developed models were tested for robustness using various validation techniques. Furthermore, a 5-ns molecular dynamics (MD) simulation and binding free energy calculations were carried out to determine the binding modes of the inhibitors and to identify crucial interacting residues. The rationality and stability of molecular docking and 3D-QSAR results were validated by MD simulation. The binding free energies calculated by the MM/PBSA method showed the importance of the van der Waals interaction.

CONCLUSIONS

A good correlation between the MD results, docking studies, and the contour map analysis were observed. The study has identified the key amino acid residues in Btk binding pocket. The results from this study can provide some insights into the development of potent, novel Btk inhibitors.

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

HIGHLIGHTS

Many CNS targets are being explored for multi-target drug design

New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds

QSAR, virtual screening and docking methods increase the potential of rational drug design

The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D₁-R/D₂-R/5-HT_2A-R/H₃-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.

Molecular modeling studies on series of Btk inhibitors using docking, structure-based 3D-QSAR and molecular dynamics simulation: a combined approach

Bruton tyrosine kinase (Btk) is a non-receptor tyrosine kinase. It is a crucial component in BCR pathway and expressed only in hematopoietic cells except T cells and Natural killer cells. BTK is a promising target because of its involvement in signaling pathways and B cell diseases such as autoimmune disorders and lymphoma. In this work, a combined molecular modeling study of molecular docking, 3D-QSAR and molecular dynamic (MD) simulation were performed on a series of 2,5-diaminopyrimidine compounds as inhibitors targeting Btk kinase to understand the interaction and key residues involved in the inhibition. A structure based CoMFA (q (2) = 0.675, NOC = 5, r (2) = 0.961) and COMSIA (q (2) = 0.704, NOC = 6, r (2) = 0.962) models were developed from the conformation obtained by docking. The developed models were subjected to various validation techniques such as leave-five-out, external test set, bootstrapping, progressive sampling and rm (2) metrics and found to have a good predictive ability in both internal and external validation. Our docking results showed the important residues that interacts in the active site residues in inhibition of Btk kinase. Furthermore, molecular dynamics simulation was employed to study the stability of the docked conformation and to investigate the binding interactions in detail. The MD simulation analyses identified several important hydrogen bonds with Btk, including the gatekeeper residue Thr474 and Met477 at the hinge region. Hydrogen bond with active site residues Leu408 and Arg525 were also recognized. A good correlation between the MD results, docking studies and the contour map analysis are observed. This indicates that the developed models are reliable. Our results from this study can provide insights in the designing and development of more potent Btk kinase inhibitors.

Scaffold hopping of potential anti-tumor agents by WEGA: a shape-based approach

In this paper, we describe the first prospective application of the shape-comparison program, WEGA (weighted Gaussian algorithm), to find new scaffolds for anti-tumor agents.

In silico study of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanones derivatives as CCR1 antagonist: homology modeling, docking and 3D-QSAR approach

C-C chemokine receptor type 1 (CCR1) is a chemokine receptor with seven transmembrane helices and it belongs to the G-Protein Coupled receptor (GPCR) family. It plays an important role in rheumatoid arthritis, organ transplant rejection, Alzheimer's disease and also causes inflammation. Because of its role in disease processes, CCR1 is considered to be an important drug target. In the present study, we have performed three dimensional Quantitative Structure activity relationship (3D-QSAR) studies on a series of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanone derivatives targeting CCR1. Homology modeling of CCR1 was performed based on a template structure (4EA3) which has a high sequence identity and resolution. The highest active molecule was docked into this model. Ligand-based and Receptor-based quantitative structure-activity relationship (QSAR) study was performed and CoMFA models with reasonable statistics was developed for both ligand-based (q(2)=0.606; r(2)=0.968) and receptor-guided (q(2)=0.640; r(2)=0.932) alignment methods. Contour map analyses identified favorable regions for high affinity binding. The docking results highlighted the important active site residues. Tyr113 was found to interact with the ligand through hydrogen bonding. This residue has been considered responsible for anchoring ligands inside the active site. Our results could also be helpful to understand the inhibitory mechanism of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanone derivatives thereby to design more effective ligands in the future.