Narrow Results

A robust pharmacophore model was developed and the structure-activity relationship was analyzed using 71 pyrimidine derivatives reported for covalent Janus Kinase 3 (JAK3) inhibition. Pharmacophore modeling developed a five featured pharmacophore: one H-bond acceptor, two H-bond donors, one hydrophobic, and one aromatic ring features. The atom-based three-dimensional QSAR models with statistical significance were generated using the training set of 52 compounds. The excellent predictive correlation coefficients were obtained for 3D models determined using a test set of 19 molecules. The generated QSAR model implies that the hydrophobic character is important for the JAK3 inhibitory activity of these compounds. Additionally, electron-withdrawing and hydrogen bond donor groups at specific positions positively contribute to the JAK3 inhibition potency. These results provided essential three-dimensional structural requirements and the crucial binding features of 2,4-disubstituted pyrimidine derivatives, which may direct for the design and discovery of novel potent JAK3 inhibitors.

Sonic hedgehog (Shh) plays an important role in the activation of Shh signaling pathway that regulates preservation and rebirth of adult tissues. An abnormal activation of this pathway has been identified in hyperplasia and various tumorigenesis. Hence the inhibition of this pathway using a Shh inhibitor might be an efficient way to treat a wide range of malignancies. This study was done in order to develop a lead chemical candidate that has an inhibitory function in the Shh signaling pathway. We have generated common feature pharmacophore models using three-dimensional (3D) structural information of robotnikinin, an inhibitor of the Shh signaling pathway, and its analogs. These models have been validated with fit values of robotnikinin and its analogs, and the best model was used as a 3D structural query to screen chemical databases. The hit compounds resulted from the screening docked into a proposed binding site of the Shh named pseudo-active site. Molecular dynamics (MD) simulations were performed to investigate detailed binding modes and molecular interactions between the hit compounds and functional residues of the pseudo-active site. The results of the MD simulation analyses revealed that the hit compounds can bind the pseudo-active site with high affinity than robotnikinin. As a result of this study, a candidate inhibitor (GK 03795) was selected as a potential lead to be employed in future Shh inhibitor design.

The genome sequence of Plasmodium falciparum reveals that many metabolic pathways are unique as compared to its human host. Metabolic Network Analysis was carried out to find the essential enzymes critical for the survival of the pathogen. In the present study, choke point and load point analysis was used to locate putative targets. The identified targets were further checked to confirm that no alternate pathway or human homolog exists. Among the top 15 enzymes obtained from this analysis, we have selected P. falciparum orotidine-5'-monophosphate decarboxylase (PfODCase) enzyme as it is sequentially and structurally different from that of humans, for searching novel inhibitors. A five-point 3D pharmacophore was generated for the crystal structure of PfODCase complexes with uridine-5'-monophosphate (U5P). The binding site environment shows three H-bond acceptors, one H-bond donor and one negative ionizable feature. This pharmacophore model was used as a 3D query to perform virtual screening experiments against 2,664,779 standard lead compounds obtained from the freely available ZINC database. Top 10 hits obtained from virtual screening were selected for molecular docking experiments against PfODCase in order to verify their results and to have a better insight into their binding modes. Here, docking of U5P with PfODCase is used as a control. We have identified six compounds, among them, few are U5P analogs and others are novel ones with diverse scaffolds. The key residues: Lys42, Asp20, Lys72, Ser127, Ala184, Gln185 and Arg203 at the main binding pocket of PfODCase are responsible for better stability of diverse ligands. These compounds according to their free energy of binding could serve as potent leads for designing novel inhibitors against malarial ODCase enzyme.

Dengue virus (DENV) is the causative agent of dengue fever, dengue hemorrhagic disease and dengue shock syndrome (DSS), transmitted predominantly in tropical and subtropical regions by Aedes aegypti. It infects millions of people and causes thousands of deaths each year, but there is no antiviral drug against DENV. Usnic acid lately piqued the interest of researchers for extraordinary biological characteristics, including antiviral activity. Based on high larvicidal activities against Aedes aegypti, this study aims to search usnic acid derivatives as novel anti-DENV agents through a combination of ligand-based and pharmacophore-based virtual screening. One hundred and sixteen (116) usnic acid derivatives were obtained from a database of 428 in-house usnic acid derivatives through pharmacophore filtering steps. Subsequent docking simulation on DENV-3 NS-5 RdRp afforded 41 compounds with a strong binding affinity towards the enzyme. The pharmacokinetics and drug likeness prediction resulted in seven hit compounds, which eventually undergo cytochrome P450 enzyme screening to obtain the lead compound, labelled as 362. In addition, molecular dynamic (MD) simulation of lead compound 362 was performed to verify the stability of the docked complex and the binding posture acquired in docking experiments. Overall, the lead compounds have shown a high fit value of pharmacophore, strong binding affinity towards RdRp enzyme, good pharmacokinetics, and drug likeness properties. The discovery of a new usnic acid derivative as a novel anti-DENV agent targeting RdRp could lead to further drug development and optimization to treat dengue.

Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the $-10$kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

The SARS-CoV-2 main protease (M^pro) is one of the essential therapeutic keys of the COVID-19, which ensures the replication of the virus in the host cells, causing tissue damage, for this reason, we accelerate the research of potential natural compounds that could inhibit this enzyme or used as a scaffold to generate a series of optimized and potent inhibitors. In silico study was performed. This study was started with the generation and validation of a structure-based pharmacophore hypothesis that is used to screen a chemical library containing 99,049 natural compounds extracted, filtered, and cleaned from the Chinese universal natural products database (UNPD), the screening process using the pharmacophore hypothesis and the molecular docking yielded 15 hits. These hits were ranked depending on their binding $\mathrm{\Delta }G$, as a result, the two compounds UNPD90246 and UNPD221225 exhibited higher binding affinity to the catalytic cavity of the M^pro interacting with important amino acids which play a crucial role in the enzyme catalysis, on the other hand, the two complexes UNPD90246/5R82 and UNPD221225/5R82 subjected to the molecular dynamic simulation show very good stability along the trajectory of the simulation as revealed by the calculated RMSD, RMSF, rGyr, SASA as well as the RMSF of the protein and the potential energy. In addition, the in silico evaluation of some drug-like and pharmacokinetic (ADMET) properties, revealed in general satisfactory prediction results for almost all compounds, however, some compounds require optimization steps to improve their weakness, regarding pharmacokinetic properties such as metabolism and toxicity.

Hydrazones typically have an azomethine ‘–NHN=CH group’ which is crucial for varieties of pharmacological activities. In this study, we have synthesized a set of 15 new hydrazones (5A–5O) of 2-((2,3-dihydro-1H-inden-4-yl)oxy)acetohydrazide, which were characterized with various spectroscopic methods. It was found that the compound 5H had significant bioactivities (Antioxidant: DPPH assay: 80.3% inhibition; at 100$\mu$g/mL concentration; Antibacterial: Minimum inhibitory concentration (MIC) of 6.25$\mu$g/mL against E. coli; Antimycobacterial, H37Rv Vaccine strain: MIC of 3.12$\mu$g/mL; Anticancer: Trypan Blue assay, MCF-7; IC${}_{50}$ values of 84.3mM). To understand the probable underlying antibacterial mechanisms, we examined all hydrazones against a common bacterial target (2,2-dialkylglycine decarboxylase, PDB ID: 1d7u) and pantothenate synthase from Mycobacteria (PDB ID: 3ivx) using molecular docking. Our molecular dynamics simulations for 100ns for both the complexes (5H:1d7u and 5H:3ivx) revealed significant stability of ligand–protein complex. Moreover, to correlate structural features with biological activity, we have developed statistically significant 5-parametric Quantitative Structure–Activity Relationships (QSAR) models. The pharmacophore hypothesis, AADHR_1 (5-point) signifies its importance for biological activities. Finally, we have examined the theoretical and pharmacokinetics and toxicity properties of all synthesized compounds.

Glucose transporter 1 (GLUT1) plays an important role in the transport of glucose into cells. Studies found that GLUT1 is highly expressed in a variety of tumor cells, and it’s the rate-limiting transporter for tumor cells to uptake glucose, meaning that GLUT1 is a potential target for tumor treatment. Thus, the inhibition of GLUT1 by novel small compounds to lower glucose levels for cancer cells has become an emerging strategy. In this study, potential GLUT1 inhibitors were discovered by pharmacophore-based high-throughput virtual screening. First, 96 GLUT1 inhibitors were used to establish common pharmacophore hypotheses. The best pharmacophore model ADHRRR_1 was used to screen the ZINC database. Then, ADMET was launched to predict the pharmacokinetics of hit compounds from virtual screening. After that, the standard docking and precise docking were employed to dock these compounds with 4PYP protein; five molecules were selected as candidates of GLUT1 inhibitors for further analysis. Molecular Dynamic (MD) simulations and Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) method were applied to evaluate the binding stability and affinity of the five protein–ligand composites. Finally, Zinc000068152777 and Zinc000141918576 with lower docking scores and binding free energy emerged as the potential GLUT1 inhibitors. Taken together, these results in our study may provide valuable information for cancer therapeutics by disturbing the energy metabolism of tumors.

This study explored a series of reported 5-lipoxygenase-activating protein (FLAP) inhibitors to understand their structural requirements and identify potential new inhibitor scaffolds through automated unbiased procedures. Docking studies have revealed that inhibitor binding affinity can be influenced by several key binding interactions with Phe114 and Lys116 from chain B and Val21, Phe25, His28 and Lys29 from chain C in the FLAP-binding site. A ligand-based alignment three-dimensional (3D)-quantitative structure–activity relationship (QSAR) was adopted, resulting in a robust model with a statistically significant noncross-validated coefficient ($r^2=0.992$), a cross-validated correlation coefficient ($q^2=0.681$) and a predictive squared correlation coefficient ($r_{\mathrm{\text{pred}}}^2=0.736$). Overall, the analysis revealed the important electrostatic and steric attributes responsible for the FLAP inhibitory activity, which appeared to correlate well with the docking results. In addition, two statistically significant two-dimensional (2D)-QSAR models ($r^2=0.9369$, $q^2=0.889$ and $r^2=0.9679$, $q^2=0.655$) were developed by a genetic function approximation (GFA). HypoGen 1, a proposed pharmacophore model, was used for database mining to identify potential new FLAP inhibitors. The bioactivity of the retrieved hits was then evaluated in silico based on the validated QSAR models, followed by pharmacokinetics and toxicity predictions.

Infectious diseases caused by viruses are one of the major diseases that seriously endanger global health. The development of broad-spectrum antiviral drugs for effective control of emerging and recurrent viral infectious diseases is urgently needed. Fatty acid synthase (FAS) has been reported as a valuable target for developing broad-spectrum antiviral drugs. In this study, we aimed to screen potent FAS inhibitors as broad-spectrum antiviral agents through computational approach. A pharmacophore model was generated with crystal structure of FAS in complex with Orlistat using Pharmit server, and 2,215 compounds were screened from ZINC15 database against the pharmacophore model. Next, 12 compounds exhibited more affinity to FAS than the control compound (Orlistat) were screened using molecular docking. The top three compounds (ZINC6146291, ZINC8925941 and ZINC3257427), which showed docking affinity to FAS with −8.19 kcal/mol, −8.14 kcal/mol and −8.07 kcal/mol, respectively, were subjected to 200 ns molecular dynamics simulation to evaluate the stability of docked complexes and their interaction mechanisms. The simulation trajectory analysis showed the stable conformation observed in FAS bound of the compounds and the screened compounds were firmly bound of the active sites of FAS throughout molecular dynamics simulation. The screened FAS candidate inhibitors can be used as broad-spectrum antiviral agents for in vitro-in vivo evaluations.

Ubiquitin-specific protease-7 (USP7) is a potential target for cancer therapy. In this work, the evaluation and design of USP7 inhibitors are conducted employing a comprehensive computational approach. Initially, a large database of compounds undergoes preliminary screening via similarity and pharmacophore analyses. Subsequently, a secondary selection process is executed based on both quantitative structure–activity relationship modeling and molecular docking. The final selection is refined through rescoring treatments via consensus scores and MM/GBSA binding free energy. A total of 138 experimental USP7 inhibitors were collected for the construction of random forest regression models using four different feature selection methods. 17 top experimentally active inhibitors were used as models in similarity searching, and eight representative USP7-ligand binding models were used in pharmacophore screening, performed over a database of 14 million compounds. The rescoring approach applied in this study offers an alternative and beneficial method for the ranking and selection of desired molecules. Based on the molecular scaffolds of the highly active inhibitors, some new derivatives have been designed and received high predicted scores for inhibition.