In silico studies on potential MCF-7 inhibitors: a combination of pharmacophore and 3D-QSAR modeling, virtual screening, molecular docking, and pharmacokinetic analysis

Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors

The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R2 = 0.974, Q2 = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R2pred = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA.Communicated by Ramaswamy H. Sarma.

Computer-Aided Strategy on 5-(Substituted benzylidene) Thiazolidine-2,4-Diones to Develop New and Potent PTP1B Inhibitors: QSAR Modeling, Molecular Docking, Molecular Dynamics, PASS Predictions, and DFT Investigations

A set of 5-(substituted benzylidene) thiazolidine-2,4-dione derivatives was explored to study the main structural requirement for the design of protein tyrosine phosphatase 1B (PTP1B) inhibitors. Utilizing multiple linear regression (MLR) analysis, we constructed a robust quantitative structure-activity relationship (QSAR) model to predict inhibitory activity, resulting in a noteworthy correlation coefficient (R2) of 0.942. Rigorous cross-validation using the leave-one-out (LOO) technique and statistical parameter calculations affirmed the model's reliability, with the QSAR analysis revealing 10 distinct structural patterns influencing PTP1B inhibitory activity. Compound 7e(ref) emerged as the optimal scaffold for drug design. Seven new PTP1B inhibitors were designed based on the QSAR model, followed by molecular docking studies to predict interactions and identify structural features. Pharmacokinetics properties were assessed through drug-likeness and ADMET studies. After that density functional theory (DFT) was conducted to assess the stability and reactivity of potential diabetes mellitus drug candidates. The subsequent dynamic simulation phase provided additional insights into stability and interactions dynamics of the top-ranked compound 11c. This comprehensive approach enhances our understanding of potential drug candidates for treating diabetes mellitus.

A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets' stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (μ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO-LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive.Communicated by Ramaswamy H. Sarma.

Gallic Acid Ameliorates Cognitive Impairment Caused by Sleep Deprivation through Antioxidant Effect

Sleep deprivation (SD) has a profound impact on the central nervous system, resulting in an array of mood disorders, including depression and anxiety. Despite this, the dynamic alterations in neuronal activity during sleep deprivation have not been extensively investigated. While some researchers propose that sleep deprivation diminishes neuronal activity, thereby leading to depression. Others argue that short-term sleep deprivation enhances neuronal activity and dendritic spine density, potentially yielding antidepressant effects. In this study, a two-photon microscope was utilized to examine the calcium transients of anterior cingulate cortex (ACC) neurons in awake SD mice in vivo at 24-hour intervals. It was observed that SD reduced the frequency and amplitude of Ca2+ transients while increasing the proportions of inactive neurons. Following the cessation of sleep deprivation, neuronal calcium transients demonstrated a gradual recovery. Moreover, whole-cell patch-clamp recordings revealed a significant decrease in the frequency of spontaneous excitatory post-synaptic current (sEPSC) after SD. The investigation also assessed several oxidative stress parameters, finding that sleep deprivation substantially elevated the level of malondialdehyde (MDA), while simultaneously decreasing the expression of Nuclear Factor erythroid 2-Related Factor 2 (Nrf2) and activities of Superoxide dismutase (SOD) in the ACC. Importantly, the administration of gallic acid (GA) notably mitigated the decline of calcium transients in ACC neurons. GA was also shown to alleviate oxidative stress in the brain and improve cognitive impairment caused by sleep deprivation. These findings indicate that the calcium transients of ACC neurons experience a continuous decline during sleep deprivation, a process that is reversible. GA may serve as a potential candidate agent for the prevention and treatment of cognitive impairment induced by sleep deprivation.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

Detection of Natural Inhibitors against Human Liver Cancer Cell Lines through QSAR, Molecular Docking and ADMET Studies

BACKGROUND

Liver cancer is ranked as the fifth most prevalent and third most lethal cancer worldwide. The incidence rates of this cancer are on the rise, and only limited treatment options are available.

METHODS

To identify and optimize the inhibitors of liver cancer cell-lines, a QSAR model was developed by using multiple linear regression methods. The robustness of the model was validated through statistical methods and wet-lab experiments.

RESULTS

The developed QSAR models yielded high activity descriptor relationship accuracy of 91%, referred to by regression coefficient (r²= 0.91), and a high activity prediction accuracy of 89%. The external predicted (pred_r²) ability of the model was found to be 90%.

CONCLUSION

The QSAR study indicates that chemical descriptors such as to measure of electronegative atom count (Epsilon3), atom type count descriptors (MMFF_10), number of a carbon atom connected with four single bonds (SssssCE- index), molecular weight and, number of oxygen atom connected with two aromatic bonds (SaaOE-index) are significantly correlated with anticancer activity. The model, which was validated statistically and through wet-lab experiments, was further used in the virtual screening of potential inhibitors against the liver cancer cell line WRL68. ADMET risk screening, synthetic accessibility, and Lipinski's rule of five are used to filter false positive hits. AfterwardS, to achieve a set of aligned ligand poses and rank the predicted active compounds, docking studies were carried out. The studied compounds and their metabolites were also analyzed for different pharmacokinetics parameters. Finally, a series of compounds was proposed as anticancer agents.

Synthetic and medicinal chemistry of phthalazines: Recent developments, opportunities and challenges

Fused diaza-heterocycles constitute the core structure of numerous bioactive natural products and effective therapeutic drugs. Among them, phthalazines have been recognized as remarkable structural leads in medicinal chemistry due to their wide application in pharmaceutical and agrochemical industries. Accessing such challenging pharmaceutical agents/drug candidates with high chemical complexity through synthetically efficient approaches remains an attractive goal in the contemporary medicinal chemistry and drug discovery arena. In this review, we focus on the recent developments in the synthetic routes towards the generation of phthalazine-based active pharmaceutical ingredients and their biological potential against various targets. The general reaction scope of these innovative and easily accessible strategies was emphasized focusing on the functional group tolerance, substrate and coupling partner compatibility/limitation, the choice of catalyst, and product diversification. These processes were also accompanied by the mechanistic insights where deemed appropriate to demonstrate meaningful information. Moreover, the rapid examination of the structure-activity relationship analyses around the phthalazine core enabled by the pharmacophore replacement/integration revealed the generation of robust, efficient, and more selective compounds with pronounced biological effects. A large variety of in silico methods and ADME profiling tools were also employed to provide a global appraisal of the pharmacokinetics profile of diaza-heterocycles. Thus, the discovery of new structural leads offers the promise of improving treatments for various tropical diseases such as tuberculosis, leishmaniasis, malaria, Chagas disease, among many others including various cancers, atherosclerosis, HIV, inflammatory, and cardiovascular diseases. We hope this review would serve as an informative collection of structurally diverse molecules enabling the generation of mature, high-quality, and innovative routes to support the drug discovery endeavors.

Effects of Different Dietary Flavonoids on Dipeptidyl Peptidase-IV Activity and Expression: Insights into Structure-Activity Relationship

The inhibitory effects of 30 dietary flavonoids on dipeptidyl peptidase-IV (DPP-IV) were investigated to illustrate their quantitative structure-activity relationship (QSAR) and further explore their inhibition at the cellular level. Results of in vitro experiment show that isorhamnetin-3-O-glucoside (IC₅₀, 6.53 ± 0.280 μM) had the strongest inhibition followed by cyanidin-3-O-glucoside (IC₅₀, 8.26 ± 0.143 μM) and isorhamnetin-3-O-rutinoside (IC₅₀, 8.57 ± 0.422 μM). A 3D QSAR model [comparative molecular field analysis, q² = 0.502, optimum number of components (ONC) = 3, R² = 0.983, F = 404.378, standard error of estimation (SEE) = 0.070, and two descriptors; comparative similarity index analysis, q² = 0.580, ONC = 10, R² = 0.999, F = 1617.594, SEE = 0.022, and four descriptors] indicates that the DPP-IV inhibition of flavonoid was facilitated by crucial structural factors. Position 3 of ring C favored bulky, hydrogen bond acceptors and hydrophilic and electron-donating substituents. The presence of minor and electron-withdrawing groups at position 4' of ring B and positions 5 and 7 of ring A could improve DPP-IV inhibition. Moreover, the three flavonoids mentioned above could effectively suppress DPP-IV activity and expression in Caco-2 cells. This work may supply new insights into dietary flavonoids as DPP-IV inhibitors for controlling blood glucose.

Synthesis, molecular modelling and anticancer evaluation of new pyrrolo[1,2-b]pyridazine and pyrrolo[2,1-a]phthalazine derivatives

Two new series of heterocyclic derivatives with potential anticancer activity, in which a pyrrolo[1,2-b]pyridazine or a pyrrolo[2,1-a]phthalazine moiety was introduced in place of the 3'-hydroxy-4'-methoxyphenyl ring of phenstatin have been synthesised and their structure-activity relationship (SAR) was studied. Fourteen of the new compounds were evaluated for their in vitro cytotoxic activity by National Cancer Institute (NCI) against 60 human tumour cell lines panel. The best five compounds in terms of in vitro growth inhibition were screened in the second stage five dose-response studies, three of them showing a very good antiproliferative activity with GI50<100 nM on several cell lines including colon, ovarian, renal, prostate, brain and breast cancer, melanoma and leukemia. Docking experiments on the biologically active compounds showed a good compatibility with the colchicine binding site of tubulin.

Combined approach of homology modeling, molecular dynamics, and docking: computer-aided drug discovery

With the continuous development in software, algorithms, and increase in computer speed, the field of computer-aided drug design has been witnessing reduction in the time and cost of the drug designing process. Structure based drug design (SBDD), which is based on the 3D structure of the enzyme, is helping in proposing novel inhibitors. Although a number of crystal structures are available in various repositories, there are various proteins whose experimental crystallization is difficult. In such cases, homology modeling, along with the combined application of MD and docking, helps in establishing a reliable 3D structure that can be used for SBDD. In this review, we have reported recent works, which have employed these three techniques for generating structures and further proposing novel inhibitors, for cytoplasmic proteins, membrane proteins, and metal containing proteins. Also, we have discussed these techniques in brief in terms of the theory involved and the various software employed. Hence, this review can give a brief idea about using these tools specifically for a particular problem.

In silico studies on p21-activated kinase 4 inhibitors: comprehensive application of 3D-QSAR analysis, molecular docking, molecular dynamics simulations, and MM-GBSA calculation

P21-activated kinase 4 (PAK4) is a serine/threonine protein kinase, which is associated with many cancer diseases, and thus being considered as a potential drug target. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations were performed to explore the structure-activity relationship of a series of pyrropyrazole PAK4 inhibitors. The statistical parameters of comparative molecular field analysis (CoMFA, Q ² = 0.837, R ² = 0.990, and R ² _pred = 0.967) and comparative molecular similarity indices analysis (CoMSIA, Q ² = 0.720, R ² = 0.972, and R ² _pred = 0.946) were obtained from 3D-QSAR model, which exhibited good predictive ability and significant statistical reliability. The binding mode of PAK4 with its inhibitors was obtained through molecular docking study, which indicated that the residues of GLU396, LEU398, LYS350, and ASP458 were important for activity. Molecular mechanics generalized born surface area (MM-GBSA) method was performed to calculate the binding free energy, which indicated that the coulomb, lipophilic and van der Waals (vdW) interactions made major contributions to the binding affinity. Furthermore, through 100 ns MD simulations, we obtained the key amino acid residues and the types of interactions they participated in. Based on the constructed 3D-QSAR model, some novel pyrropyrazole derivatives targeting PAK4 were designed with improved predicted activities. Pharmacokinetic and toxicity predictions of the designed PAK4 inhibitors were obtained by the pkCSM, indicating these compounds had better absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Above research provided a valuable insight for developing novel and effective pyrropyrazole compounds targeting PAK4.

Pharmacophore modeling, 3D-QSAR and molecular docking studies of quinazolines and aminopyridines as selective inhibitors of inducible nitric oxide synthase

Pharmacophore modeling and 3D-Quantitative Structure Activity Relationship (3D-QSAR) studies have been performed on a dataset of thirty-two quinazoline and aminopyridine derivatives to get an insight into the important structural features required for binding to inducible nitric oxide synthase (iNOS). A four-point CPH (Common Pharmacophore Hypothesis), AHPR.29, with a hydrogen bond acceptor, hydrophobic group, positively charged ionizable group and an aromatic ring, has been obtained as the best pharmacophore model. Satisfactory statistical parameters of correlation ([Formula: see text]) and cross-validated ([Formula: see text]) correlation coefficients, 0.9288 and 0.6353, respectively, show high robustness and good predictive ability of our selected model. The contour maps have been developed from this model and the analysis has provided an interpretable explanation of the effect that various features and substituents have on the potency and selectivity of inhibitors towards iNOS. Docking studies have also been performed in order to analyze the interactions between the enzyme and the inhibitors. Our proposed model can thus be further used for screening a large database of compounds and design new iNOS inhibitors.

An insight into selective and potent inhibition of histone deacetylase 8 through induced-fit docking, pharmacophore modeling and QSAR studies

Histone deacetylase 8 (HDAC8) has emerged as an important therapeutic target due to its involvement in various cancerous and neurodegenerative disease states. Since pan HDAC inhibition has been linked to various side effects, the need of the hour is to develop inhibitors truly selective for one isoform. This work attempts to explore the structural basis of selective HDAC8 inhibition by docking, pharmacophore and 3 D QSAR studies of 53 highly potent and highly selective triazol-based hydroxamic acid inhibitors. The binding modes of these novel inhibitors have been explored via Glide XP (Extra Precision) and induced-fit docking (IFD) strategies. The IFD poses of highly active and selective inhibitors showed conformational changes in active site residues like Trp141, Phe152 and Phe208, which were further verified by molecular dynamics simulations. A new CH-π interaction, which is atypical of HDAC inhibitors, was also observed in case of some highly selective inhibitors. Two pharmacophore models have been proposed; one highlights the structural basis of potency of these inhibitors and the other focuses on the selectivity. The corresponding QSAR models, obtained from alignment of the inhibitors as per the proposed pharmacophore models, are highly statistically significant. These models highlight the importance of size of the hydrophobic and aromatic groups present in the inhibitors and their contribution to activity of the inhibitors. The ADMET properties of the ligand library have also been analyzed and the predicted descriptors have been correlated with activity using principal components analysis to gain insight into the effect of pharmacokinetic properties on the activity.Communicated by Ramaswamy H. Sarma.

Identification of novel urease inhibitors: pharmacophore modeling, virtual screening and molecular docking studies

Pharmacophore modeling and atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR) have been developed on N-acylglycino- and hippurohydroxamic acid derivatives, which are known potential inhibitors of urease. This is followed by virtual screening and ADMET (absorption, distribution, metabolism, excretion and toxicity) studies on a large library of known drugs in order to get lead molecules as Helicobacter pylori urease inhibitors. A suitable three-featured pharmacophore model comprising one H-bond acceptor and two H-bond donor features (ADD.10) has been found to be the best QSAR model. An external library of compounds (∼3000 molecules), pre-filtered using Lipinski's rule of five, has been further screened using the pharmacophore model ADD.10. By analyzing the fitness of the hits with respect to the pharmacophore model and their binding interaction inside the urease active site, four molecules have been predicted to be extremely good urease inhibitors. Two of these have significant potential and should be taken up for further drug-designing process.

Pharmacophore modeling, multiple docking, and molecular dynamics studies on Wee1 kinase inhibitors

Wee1-like protein kinase (Wee1) is a tyrosine kinase that regulates the G₂ checkpoint and prevents entry into mitosis in response to DNA damage. Based on a series of signaling pathways initiated by Wee1, Wee1 has been recognized as a potential target for cancer therapy. To discover potent Wee1 inhibitors with novel scaffolds, ligand-based pharmacophore model has been built based on 101 known Wee1 inhibitors. Then the best pharmacophore model, AADRRR.340, with good partial least square (PLS) statistics (R² = 0.9212, Q² = 0.7457), was selected and validated. The validated model was used as a three-dimensional (3D) search query for databases virtual screening. The filtered molecules were further analyzed and refined by Lipinski's rule of 5, multiple docking procedures (high throughput virtual screening (HTVS), standard precision (SP), genetic optimization for ligand docking (GOLD), extra precision (XP), and unique quantum polarized ligand docking (QPLD)); absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening; and the Prime/molecular mechanics generalized born surface area (MM-GBSA) method binding free energy calculations. Eight leads were identified as potential Wee1 inhibitors, and a 50 ns molecular dynamics (MD) simulation was carried out for top four inhibitors to predict the stability of ligand-protein complex. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) based on MD simulation and the energy contribution per residue to the binding energy were calculated. In the end, three hits with good stabilization and affinity to protein were identified. Communicated by Ramaswamy H. Sarma.

In silico exploration of aryl sulfonamide analogs as voltage-gated sodium channel 1.7 inhibitors by using 3D-QSAR, molecular docking study, and molecular dynamics simulations

It has been demonstrated by human genetics that the voltage-gated sodium channel Nav1.7 is currently a promising target for the treatment of pain. In this research, we performed molecular simulation works on a series of classic aryl sulfonamide Nav1.7 inhibitors using three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for the first time to explore the correlation between their structures and activities. The results of the relevant statistical parameters of comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analyses (CoMSIA) had been verified to be reasonable, and the deep relationship between the structures and activities of these inhibitors was obtained by analyzing the contour maps. The generated 3D-QSAR model showed a good predictive ability and provided valuable clues for the rational modification of molecules. The interactions between compounds and proteins were modeled by molecular docking studies. Finally, accuracy of the docking results and stability of the complexes were verified by 100 ns MD simulations. Detailed information on the key residues at the binding site and the types of interactions they participate in involved was obtained. The van der Waals energy contributed the most in the molecular binding process according to the calculation of binding free energy. All research results provided a good basis for further research on novel and effective Nav1.7 inhibitors.

Identification of chemically diverse GABAA agonists as potential anti-epileptic agents using structure-guided virtual screening, ADMET, quantum mechanics and clinical validation through off-target analysis

Development of resistance against existing anti-epileptic drugs has alarmed the scientific innovators to find novel potential chemical starting points for the treatment of epilepsy and GABA_A inhibition is a promising drug target strategy against epilepsy. The crystal structure of a subtype-selective β3-homopentameric ligand-gated ion channel of GABA_A receptor has been used for the first time for screening the Asinex library for discovery of GABA_A agonists as potential anti-epileptic agents. Co-crystallized ligand established the involvement of part of the β7-β8 loop (Glu155 and Tyr157) and β9-β10 loop (Phe200 and Tyr205) residues as the crucial amino acids in effective binding, an essential feature, being hydrogen bond or ionic interaction with Glu155 residue. Top ranked hits were further subjected to binding energy estimation, ADMET analysis and ligand efficiency matric calculations as consecutive filters. About 19 compounds qualifying all parameters possessed interaction of one positively charged group with Glu155 with good CNS drug-like properties. Simulation studies were performed on the apo protein, its complex with co-crystallized ligand and the best hit qualifying all screening parameters. The best hit was also analyzed using Quantum mechanical studies, off-target analysis and hit modification. The off-target analysis emphasized that these agents did not have any other predicted side-effects.

Pharmacokinetic parameters explain the therapeutic activity of antimicrobial agents in a silkworm infection model

Poor pharmacokinetic parameters are a major reason for the lack of therapeutic activity of some drug candidates. Determining the pharmacokinetic parameters of drug candidates at an early stage of development requires an inexpensive animal model with few associated ethical issues. In this study, we used the silkworm infection model to perform structure-activity relationship studies of an antimicrobial agent, GPI0039, a novel nitrofuran dichloro-benzyl ester, and successfully identified compound 5, a nitrothiophene dichloro-benzyl ester, as a potent antimicrobial agent with superior therapeutic activity in the silkworm infection model. Further, we compared the pharmacokinetic parameters of compound 5 with a nitrothiophene benzyl ester lacking chlorine, compound 7, that exerted similar antimicrobial activity but had less therapeutic activity in silkworms, and examined the metabolism of these antimicrobial agents in human liver fractions in vitro. Compound 5 had appropriate pharmacokinetic parameters, such as an adequate half-life, slow clearance, large area under the curve, low volume of distribution, and long mean residence time, compared with compound 7, and was slowly metabolized by human liver fractions. These findings suggest that the therapeutic effectiveness of an antimicrobial agent in the silkworms reflects appropriate pharmacokinetic properties.

QSAR, docking, ADMET, and system pharmacology studies on tormentic acid derivatives for anticancer activity

To explore the anticancer compounds from tormentic acid derivatives, a quantitative structure-activity relationship (QSAR) model was developed by the multiple linear regression methods. The developed QSAR model yielded a high activity-descriptors relationship accuracy of 94% referred by regression coefficient (r² = .94) and a high activity prediction accuracy of 91%. The QSAR study indicates that chemical descriptors, chiV5, T_T_Cl_7, T_2_T_4, SsCH3count, and Epsilon3 are significantly correlated with anticancer activity. This validated model was further been used for virtual screening and thus identification of new potential breast cancer inhibitors. Lipinski's rule of five, ADMET risk and synthetic accessibility are used to filter false positive hits. Filtered compounds were then docked to identify the possible target binding pocket, to obtain a set of aligned ligand poses and to prioritize the predicted active compounds. The scrutinized compounds, as well as their metabolites, were predicted and analyzed for different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Finally, the top-ranked compound NB-12 was evaluated by system pharmacology approach. Later studied the metabolic networks, disease biomarker networks, pathway maps, drug-target networks and generate significant gene networks. The strategy applied in this research work may act as a framework for rational design of potential anticancer drugs.

In silico binding mechanism prediction of benzimidazole based corticotropin releasing factor-1 receptor antagonists by quantitative structure activity relationship, molecular docking and pharmacokinetic parameters calculation

Despite the various research efforts toward the treatment of stress-related disorders, the drug has not yet launched last 20 years. Corticotropin releasing factor-1 receptor antagonists have been point of great interest in stress-related disorders. In the present study, we have selected benzazole scaffold-based compounds as corticotropin releasing factor-1 antagonists and performed 2D and 3D QSAR studies to identify the structural features to elucidating the binding mechanism prediction. The best 2D QSAR model was obtained through multiple linear regression method with r² value of .7390, q² value of .5136 and pred_r² (predicted square correlation coefficient) value of .88. The contribution of 2D descriptor, T_2_C_1 was 60% (negative contribution) and 4pathClusterCount was 40.24% (positive contribution) in enhancing the activity. Also 3D QSAR model was statistically significant with q² value of .9419 and q²_se (standard error of internal validation) value of .19. Statistical parameters results prove the robustness and significance of both models. Further, molecular docking and pharmacokinetic analysis was performed to explore the scope of investigation. Docking results revealed that the all benzazole compounds show hydrogen bonding with residue Asn283 and having same hydrophobic pocket (Phe286, Leu213, Ile290, Leu287, Phe207, Arg165, Leu323, Tyr327, Phe284, and Met206). Compound B14 has higher activity compare to reference molecules. Most of the compounds were found within acceptable range for pharmacokinetic parameters. This work provides the extremely useful leads for structural substituents essential for benzimidazole moiety to exhibit antagonistic activity against corticotropin releasing factor-1 receptors.

Investigations of FAK inhibitors: a combination of 3D-QSAR, docking, and molecular dynamics simulations studies

Focal adhesion kinase (FAK) is one kind of tyrosine kinases that modulates integrin and growth factor signaling pathways, which is a promising therapeutic target because of involving in cancer cell migration, proliferation, and survival. To investigate the mechanism between FAK and triazinic inhibitors and design high activity inhibitors, a molecular modeling integrated with 3D-QSAR, molecular docking, molecular dynamics simulations, and binding free energy calculations was performed. The optimum CoMFA and CoMSIA models showed good reliability and satisfactory predictability (with Q² = 0.663, R² = 0.987, [Formula: see text] = 0.921 and Q² = 0.670, R² = 0.981, [Formula: see text] = 0.953). Its contour maps could provide structural features to improve inhibitory activity. Furthermore, a good consistency between contour maps, docking, and molecular dynamics simulations strongly demonstrates that the molecular modeling is reliable. Based on it, we designed several new compounds and their inhibitory activities were validated by the molecular models. We expect our studies could bring new ideas to promote the development of novel inhibitors with higher inhibitory activity for FAK.