In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening.

c-KIT is a receptor tyrosine kinase reported in, small cell lung cancer and other human cancers. Marketed c-KIT inhibitors are suffering from tribulations of getting resistance and/or potential cardio toxicity. To identify potential novel c-KIT inhibitors, in the present project work, we have built an atom-featured 3D QSAR model using Schrodinger’s Maestro 9.0 molecular modeling suit. The developed 3D QSAR model ‘ADHRR.21’ is statistically significant (R2=0.8764, Q2 =0.7432) and instituted to be robust enough with good predictive accuracy, confirmed by external validation approaches, Y-randomization.

Histone deacetylase (HDAC) inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. To identify the important pharmacophoric features and correlate 3Dchemical structure with biological activity using 3D-QSAR and Pharmacophore modeling studies. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with wellassigned HDAC inhibitory activity were used for 3D-QSAR model development. Best 3D-QSAR model, which is a five partial least square (PLS) factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811), cross-validated coefficient rcv 2=0.9807 and R2 pred=0.7147 with low standard deviation (0.0952). Additionally, the selected pharmacophore model DDRRR.419 was used as a 3D query for virtual screening against the ZINC database. In the virtual screening workflow, docking studies (HTVS, SP and XP) were carried out by selecting multiple receptors (PDB ID: 1T69, 1T64, 4LXZ, 4LY1, 3MAX, 2VQQ, 3C10, 1W22). Finally, six compounds were obtained based on high scoring function (dock score -11.2278-10.2222 kcal/mol) and diverse structures. The structure activity correlation was established using virtual screening, docking, energetic based pharmacophore modelling, pharmacophore, atom based 3D QSAR models and their validation. The outcomes of these studies could be further employed for the design of novel HDAC inhibitors for anticancer activity.

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using ‘Receptor-Ligand Pharmacophore Generation’ method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r2 of 0.996; for the test set, the correlation coefficient r2 was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.

Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r2, which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r2 equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.

A structure-based approach to explore novel COX-2 inhibitors using pharmacophore modelling, 3D-QSAR analysis, virtual screening and dynamics simulation study

A 3D QSAR selectivity analysis of carbonic anhydrase (CA) inhibitors using a data set of 87 CA inhibitors is reported. After ligand minimization in the binding pockets of CA I, CA II, and CA IV isoforms, selectivity CoMFA and CoMSIA 3D QSAR models have been derived by taking the affinity differences (DeltapKi) with respect to two CA isozymes as independent variables. Evaluation of the developed 3D QSAR selectivity models allows us to determine amino acids in the respective CA isozymes that possibly play a crucial role for selective inhibition of these isozymes. We further combined the ligand-based 3D QSAR models with the docking program AUTODOCK in order to screen for novel CA inhibitors. Correct binding modes are predicted for various CA inhibitors with respect to known crystal structures. Furthermore, in combination with the developed 3D QSAR models we could successfully estimate the affinity of CA inhibitors even in cases where the applied scoring function failed. This novel strategy to combine AUTODOCK poses with CoMFA/CoMSIA 3D QSAR models can be used as a guideline to assess the relevance of generated binding modes and to accurately predict the binding affinity of newly designed CA inhibitors that could play a crucial role in the treatment of pathologies such as tumors, obesity, or glaucoma.

The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby significantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. In this study, a combination of structure-based drug carriers and molecular docking-based virtual screening was used to screen new potential FGFR1 inhibitors. Forty eight known inhibitors were collected to establish 3 D-QSAR models and pharmacophore models, investigate the relationship between the activity and conformation of compounds, and verify the efficiency of pharmacophore. In Accelrys Discovery Studio 2016, the ZINC database was filtered by Lipinski's Rule of Five and SMART's filtration. Then, Hypo01 was used for virtual screening of ZINC database. Compounds with predicted activity values less than 1 μM were molecularly docked with FGFR1 protein crystals, the docking results were observed, and the interaction between compounds and targets was studied. The absorption, distribution, metabolism and excretion (ADME) and toxicity of potential inhibitors were studied, and a compound with new structural scaffolds were obtained. It could be further studied to explore their better therapeutic effects. Communicated by Ramaswamy H. Sarma

BackgroundDevelopment of a cancerous cell takes place when it ceases to respond to growth-inhibiting signals and multiplies uncontrollably and can detach and move to other parts of the body; the process called as metastasis. A particular set of cysteine proteases are very active during cancer metastasis, Cathepsins being one of them. They are involved in tumor growth and malignancy and have also been reported to be overexpressed in tumor cell lines. In the present study, a combinatorial approach comprising three-dimensional quantitative structure-activity relationship (3D QSAR), ligand-based pharmacophore modelling and search followed by cathepsin L structure-based high throughput screening was carried out using an initial set of 28 congeneric thiosemicarbazone derivatives as cathepsin L inhibitors. A 3D QSAR was derived using the alignment of a common thiosemicarbazone substructure. Essential structural features responsible for biological activity were taken into account for development of a pharmacophore model based on 29 congeneric thiosemicarbazone derivatives. This model was used to carry out an exhaustive search on a large dataset of natural compounds. A further cathepsin L structure-based screen identified two top scoring compounds as potent anti-cancer leads.ResultsThe generated 3D QSAR model showed statistically significant results with an r2 value of 0.8267, cross-validated correlation coefficient q2 of 0.7232, and a pred_r2 (r2 value for test set) of 0.7460. Apart from these, a high F test value of 30.2078 suggested low probability of the model's failure. The pharmacophoric hypothesis chosen for searching the natural compound libraries was identified as DDHRR, where two Ds denote 2 hydrogen donors, H represents a hydrophobic group and two Rs represent aromatic rings, all of which are essential for the biological activity. We report two potential drug leads ZINC08764437 (NFP) and ZINC03846634 (APQ) obtained after a combined approach of pharmacophore-based search and structure-based virtual screen. These two compounds displayed extra precision docking scores of -7.972908 and -7.575686 respectively suggesting considerable binding affinity for cathepsin L. High activity values of 5.72 and 5.75 predicted using the 3D QSAR model further substantiated the inhibitory potential of these identified leads.ConclusionThe present study attempts to correlate the structural features of thiosemicarbazone group with their biological activity by development of a robust 3D QSAR model. Being statistically valid, this model provides near accurate values of the activities predicted for the congeneric set on which it is based. These predicted activities are good for the test set compounds making it indeed a statistically sound 3D QSAR model. The identified pharmacophore model DDHRR.8 comprised of all the essential features required to interact with the catalytic triad of cathepsin L. A search for natural compounds based on this pharmacophore followed by docking studies further screened out two top scoring candidates: NFP and AFQ. The high binding affinity and presence of essential structural features in these two compounds make them ideal for consideration as natural anti-tumoral agents. Activity prediction using 3D QSAR model further validated their potential as worthy drug candidates against cathepsin L for treatment of cancer.

There are an estimated 25,000 patients who develop small cell lung cancer per year in the United States. The currently available therapy is inadequate because >95% of patients die from their small cell lung cancer [(1][1] [, 2)][2] . The work with conventional chemotherapy has not dramatically

Background: Thiosemicarbazones belong to the group of semicarbazides, which contains a sulfur atom instead of an oxygen atom. Several studies have shown that they are effective against extracellular protozoans like Trichomonas vaginalis, Plasmodium falciparum, Trypanosoma cruzi, and other parasites. Objective: The current research involves pharmacophore model design, 3-D-QSAR, virtual screening, and docking studies, all of which are evaluated using various parameters. Methods: The present study was performed by Schrodinger software. A total of 40 ligands were selected for the development of 3D QSAR models. To predict the pIC50 values in 3D-QSAR analysis, the entire dataset was divided into two sets, training, and test sets, in a 7:3 ratio. The selected pharmacophore hypothesis has been used for the virtual screening study. Results: DHHRR_1 emerged as the best pharmacophore model with a survival score of 5.80. The 3D QSAR study showed a significant model with R2 = 0.91 and Q2 = 0.73. The series top-scoring compound 7e had a docking score of -10.44 and showed interactions with the amino acids, ARG-265, PHE-227, and LEU-531, required for activity. The developed pharmacophore model had been used for the screening of ZINC compounds, where ZINC26244107, ZINC13469100, ZINC01290725, and ZINC01350173 showed the best XP docking scores (-11.60, -11.27, -11.35, -10.52, respectively) while binding important amino acids ARG265, HIE185 and LEU 531 against plasmodium falciparum, PDB ID:5TBO. The docking study was further evaluated by taking standard drug chloroquine, showing similar binding interactions as shown by other compounds. The ADME studies showed the drug-likeness properties of the compounds. Conclusion: The results of the present study may be helpful for the future development of antimalarial compounds against Plasmodium falciparum.

The Mast/Stem cell growth factor receptor Kit (c-Kit), a Proto-oncogene c-Kit, is a tyrosine-protein kinase involved in cell differentiation, proliferation, migration, and survival. Its role in developing certain cancers, particularly gastrointestinal stromal tumors (GISTs) and acute myeloid leukemia (AML), makes it an attractive therapeutic target. Several small molecule inhibitors targeting c-Kit have been developed and approved for clinical use. Recent studies have focused on identifying and optimizing natural compounds as c-Kit inhibitors employing virtual screening. Still, drug resistance, off-target side effects, and variability in patient response remain significant challenges. From this perspective, phytochemicals could be an important resource for discovering novel c-Kit inhibitors with less toxicity, improved efficacy, and high specificity. This study aimed to uncover possible c-Kit inhibitors by utilizing a structure-based virtual screening of active phytoconstituents from Indian medicinal plants. Through the screening stages, two promising candidates, Anilinonaphthalene and Licoflavonol, were chosen based on their drug-like features and ability to bind to c-Kit. These chosen candidates were subjected to all-atom molecular dynamics (MD) simulations to evaluate their stability and interaction with c-Kit. The selected compounds Anilinonaphthalene from Daucus carota and Licoflavonol from Glycyrrhiza glabra showed their potential to act as selective binding partners of c-Kit. Our results suggest that the identified phytoconstituents could serve as a starting point to develop novel c-Kit inhibitors for developing new and effective therapies against multiple cancers, including GISTs and AML. The use of virtual screening and MD simulations provides a rational approach to discovering potential drug candidates from natural sources. Communicated by Ramaswamy H. Sarma

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease Mpro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective Mpro inhibitors against SARS-CoV-2. The 33.8 kDa Mpro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (Mpro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with Mpro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

2D and 3D QSAR models for identifying diphenylpyridylethanamine based inhibitors against cholesteryl ester transfer protein.

Alzheimer's disease (AD) is a neurodegenerative disorder associated with aging. Various enzymatic targets have been and are still being studied in an attempt to discover new drugs for the treatment of AD; however, Rab GTPases are still relatively unexplored. These enzymes regulate cellular processes by alternating of GDP and GTP nucleotides. In vitro studies have shown that the knockdown of Rab10 reduces the production of Aβ40 and Aβ42 peptides, making it a promising target for the treatment of AD. In order to identify potential Rab10 inhibitors, the structure-based virtual screening (SBVS) was used considering a subset of 80763 natural products obtained from ZINC15 database. Tertiary structure of Rab10 was obtained from the Protein Data Bank and the Autodock Vina program was used in the SBVS to filter potential bioactive substances against this enzyme. The SBVS protocol was validated by redocking the co-crystallized GNP and the binding energies of the GDP and GTP were used as controls in the pharmacodynamic analysis. Thus, it was possible to select 45 compounds with binding energy less or equal −11 kcal.mol−1. ADME/T properties of these compounds were evaluated by the SwissADME program, where it was possible to identify 6 promising molecules. The resulting complexes were subjected to molecular dynamics simulations to analyze the pharmacodynamics over time. The results suggest that the compound ZINC4090657 (derived from quinolizidine) and the compounds ZINC4000106 and ZINC0630250 (derived from coumarin) have favorable pharmacological characteristics for the inhibition of Rab10, with ZINC4090657 being the most promising one. Communicated by Ramaswamy H. Sarma

Malaria is a disease caused by Plasmodium genus. which P. falciparum is responsible for the most severe form of the disease, cerebral malaria. In 2018, 405,000 people died of malaria. Antimalarial drugs have serious adverse effects and limited efficacy due to multidrug-resistant strains. One way to overcome these limitations is the use of computational approaches for prioritizing candidates to phenotypic assays and/or in vitro assays against validated targets. Plasmodium falciparum Enoyl-ACP reductase (PfENR) is noteworthy because it catalyzes the rate-limiting step of the biosynthetic pathway of fatty acid. Thus, the study aimed to identify potential PfENR inhibitors by ligand (2D molecular similarity and pharmacophore models) and structure-based virtual screening (molecular docking). 2D similarity-based virtual screening using Tanimoto Index (> 0.45) selected 29,236 molecules from natural products subset available in ZINC database (n = 181,603). Next, 10 pharmacophore models for PfENR inhibitors were generated and evaluated based on the internal statistical parameters from GALAHAD™ and ROC/AUC curve. These parameters selected a suitable pharmacophore model with one hydrophobic center and two hydrogen bond acceptors. The alignment of the filtered molecules on best pharmacophore model resulted in the selection of 10,977 molecules. These molecules were directed to the docking-based virtual screening by AutoDock Vina 1.1.2 program. These strategies selected one compound to phenotypic assays against parasite. ZINC630259 showed EC50 = 0.12 ± 0.018 µM in antiplasmodial assays and selective index similar to other antimalarial drugs. Finally, MM/PBSA method showed stability of molecule within PfENR binding site (ΔGbinding=-57.337 kJ/mol). Communicated by Ramaswamy H. Sarma