Exploiting PubChem for Virtual Screening

Identification, characterization, and CADD analysis of Plasmodium DMAP1 reveals it as a potential molecular target for new anti-malarial discovery

Developing drug resistance in the malaria parasite is a reason for apprehension compelling the scientific community to focus on identifying new molecular targets that can be exploited for developing new anti-malarial compounds. Despite the availability of the Plasmodium genome, many protein-coding genes in Plasmodium are still not characterized or very less information is available about their functions. DMAP1 protein is known to be essential for growth and plays an important role in maintaining genomic integrity and transcriptional repression in vertebrate organisms. In this study, we have identified a homolog of DMAP1 in P. falciparum. Our sequence and structural analysis showed that although PfDMAP1 possesses a conserved SANT domain, parasite protein displays significant structural dissimilarities from human homolog at full-length protein level as well as within its SANT domain. PPIN analysis of PfDMAP1 revealed it to be vital for parasite and virtual High-throughput screening of various pharmacophore libraries using BIOVIA platform-identified compounds that pass ADMET profiling and showed specific binding with PfDMAP1. Based on MD simulations and protein-ligand interaction studies two best hits were identified that could be novel potent inhibitors of PfDMAP1 protein.

GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network

The Blood-Brain Barrier (BBB) is a selective barrier between the Central Nervous System (CNS) and the peripheral system, regulating the distribution of molecules. BBB permeability has been crucial in CNS-targeting drug development, such as glioblastoma-related drug discovery. In addition, more CNS diseases still present significant challenges, for instance, neurological disorders like Alzheimer's Disease (AD) and drug abuse. Conversely, cannabinoid drugs that do not cross the BBB are needed to avoid off-target CNS psychotropic effects. In vitro and in vivo experiments measuring BBB permeability are costly and low throughput. Computational pharmacoanalytics modeling, particularly using deep-learning Graph Neural Networks (GNNs), offers a promising alternative. GNNs excel at capturing intricate relationships in graph-based information, such as small molecular structures. In this study, we developed GNNs model for BBB permeability using the graph representation of drugs. The GNNs were compared with other algorithms using molecular fingerprints or physical-chemical descriptors. With a dataset of 1924 molecules, the best GNNs model, a convolutional graph neural network using a normalized Laplacian matrix (GCN_2), achieved a precision of 0.94, recall of 0.96, F1 score of 0.95, and MCC score of 0.77. This outperformed other machine learning algorithms with molecular fingerprints. The findings indicate that the graphic representation of small molecules combined with GNNs architecture is powerful in predicting BBB permeability with high accuracy and recall. The developed GNNs model can be utilized in the initial screening stage for new drug development.

Identification and evaluation of bioactive compounds from Azadirachta indica as potential inhibitors of DENV-2 capsid protein: An integrative study utilizing network pharmacology, molecular docking, molecular dynamics simulations, and machine learning techniques

Background

Dengue fever is a viral disease caused by the dengue flavivirus and transmitted through mosquito bites in humans. According to the World Health Organization, severe dengue causes approximately 40,000 deaths annually, and nearly 4 billion people are at risk of dengue infection. The urgent need for effective treatments against the dengue virus has led to extensive research on potential bioactive compounds.

Objective

In this study, we utilized a network pharmacology approach to identify the DENV-2 capsid protein as an appropriate target for intervention. Subsequently, we selected a library of 537 phytochemicals derived from Azadirachta indica (Family: Meliaceae), known for their anti-dengue properties, to explore potential inhibitors of this protein.

Methods

The compound library was subjected to molecular docking to the capsid protein to identify potent inhibitors with high binding affinity. We selected 81 hits based on a thorough analysis of their binding affinities, particularly those exhibiting higher binding energy than the established inhibitor ST-148. After evaluating their binding characteristics, we identified two top-scored compounds and subjected them to molecular dynamics simulations to assess their stability and binding properties. Additionally, we predicted ADMET properties using in silico methods.

Results

One of the inhibitors, [(5S,7R,8R,9R,10R,13R,17R)-17-[(2R)-2-hydroxy-5-oxo-2H-furan-4-yl]-4,4,8,10,13-pentamethyl-3-oxo-5,6,7,9,11,12,16,17-octahydrocyclopenta[a]phenanthren-7-yl] acetate (AI-59), showed the highest binding affinity at -10.4 kcal/mol. Another compound, epoxy-nimonol (AI-181), demonstrated the highest number of H-bonds with a binding affinity score of -9.5 kcal/mol. During molecular dynamics simulation studies, both compounds have exhibited noteworthy outcomes. Through molecular mechanics employing Generalized Born surface area (MM/GBSA) calculations, AI-59 and AI-181 displayed negative ΔG_bind scores of -74.99 and -83.91 kcal/mol, respectively.

Conclusion

The hit compounds identified in the present investigation hold the potential for developing drugs targeting dengue virus infections. Furthermore, the knowledge gathered from this study serves as a foundation for the structure- or ligand-based exploration of anti-dengue compounds.

Identification of an Isoxazole Derivative as an Antitubercular Compound for Targeting the FadD Enzymes of Mycobacterium tuberculosis

FadD32, a fatty acyl-AMP ligase, plays an indispensable role in mycobacterial mycolic acid synthesis and is a validated target for tuberculosis (TB) drug development. The crystal structure of Mycobacterium tuberculosis (Mtb)FadD32 has laid the foundation of structure-based drug discovery against this crucial enzyme. Here, we screened the "isoxazole" scaffold containing molecules against MtbFadD32 and identified a compound 2,4-dibromo-6-[3-(trifluoromethyl)-1,2-oxazol-5-yl]phenol (M1) with specific inhibitory activity against Mtb. Kinetics experiments showed that M1 inhibits MtbFadD32 and MtbFadD28 activity. The transcriptomics response of Mtb disclosed M1-mediated regulation of mycobacterial decisive genes involved in cell wall synthesis, consequently creating unfavorable conditions for Mtb survival. Further, M1 curtails the Mtb survival in infected macrophages and reduces Mtb burden and tubercular granulomas in a chronic infection model of BALB/c mice. Our findings provide an effective chemical scaffold to inhibit MtbFadD32 with the potential to inhibit multiple MtbFadD family of enzymes for further development as a promising candidate for treating TB.

uafR: An R package that automates mass spectrometry data processing

Chemical information has become increasingly ubiquitous and has outstripped the pace of analysis and interpretation. We have developed an R package, uafR, that automates a grueling retrieval process for gas -chromatography coupled mass spectrometry (GC -MS) data and allows anyone interested in chemical comparisons to quickly perform advanced structural similarity matches. Our streamlined cheminformatics workflows allow anyone with basic experience in R to pull out component areas for tentative compound identifications using the best published understanding of molecules across samples (pubchem.gov). Interpretations can now be done at a fraction of the time, cost, and effort it would typically take using a standard chemical ecology data analysis pipeline. The package was tested in two experimental contexts: (1) A dataset of purified internal standards, which showed our algorithms correctly identified the known compounds with R2 values ranging from 0.827-0.999 along concentrations ranging from 1 × 10-5 to 1 × 103 ng/μl, (2) A large, previously published dataset, where the number and types of compounds identified were comparable (or identical) to those identified with the traditional manual peak annotation process, and NMDS analysis of the compounds produced the same pattern of significance as in the original study. Both the speed and accuracy of GC -MS data processing are drastically improved with uafR because it allows users to fluidly interact with their experiment following tentative library identifications [i.e. after the m/z spectra have been matched against an installed chemical fragmentation database (e.g. NIST)]. Use of uafR will allow larger datasets to be collected and systematically interpreted quickly. Furthermore, the functions of uafR could allow backlogs of previously collected and annotated data to be processed by new personnel or students as they are being trained. This is critical as we enter the era of exposomics, metabolomics, volatilomes, and landscape level, high-throughput chemotyping. This package was developed to advance collective understanding of chemical data and is applicable to any research that benefits from GC -MS analysis. It can be downloaded for free along with sample datasets from Github at github.org/castratton/uafR or installed directly from R or RStudio using the developer tools: 'devtools::install_github("castratton/uafR")'.

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

Bile acids are amphiphilic substances produced naturally in humans. In the context of drug delivery and dosage form design, it is critical to understand whether a drug interacts with bile inside the gastrointestinal (GI) tract or not. This study focuses on the identification of structural fingerprints/features important for bile interaction. Molecular modelling methods such as Bayesian classification and recursive partitioning (RP) studies are executed to find important fingerprints/features for the bile interaction. For the Bayesian classification study, the ROC score of 0.837 and 0.950 are found for the training set and the test set compounds, respectively. The fluorine-containing aliphatic/aromatic group, the branched chain of the alkyl group containing hydroxyl moiety and the phenothiazine ring etc. are identified as good fingerprints having a positive contribution towards bile interactions, whereas, the bad fingerprints such as free carboxylate group, purine, and pyrimidine ring etc. have a negative contribution towards bile interactions. The best tree (tree ID: 1) from the RP study classifies the bile interacting or non-interacting compounds with a ROC score of 0.941 for the training and 0.875 for the test set. Additionally, SARpy and QSAR-Co analyses are also been performed to classify compounds as bile interacting/non-interacting. Moreover, forty-six recently FDA-approved drugs have been screened by the developed SARpy and QSAR-Co models to assess their bile interaction properties. Overall, this attempt may facilitate the researchers to identify bile interacting/non-interacting molecules in a faster way and help in the design of formulations and target-specific drug development.

Virtual Screening, Docking, and Designing of New VEGF Inhibitors as Anti-cancer Agents

BACKGROUND

VEGFR-2 tyrosine kinase inhibitors are receiving a lot of attention as prospective anticancer medications in the current drug discovery process.

OBJECTIVE

This work aims to explore the PubChem library for novel VEGFR-2 kinase inhibitors. 1H-Indazole-containing drug AXITINIB, or AG-013736 (FDA approved), is chosen as a rational molecule for drug design. This scaffold proved its efficiency in treating cancer and other diseases as well.

METHODS

The present study used the virtual screening of the database, protein preparation, grid creation, and molecular docking analyses.

RESULTS

The protein was validated on different parameters like the Ramachandran plot, the ERRAT score, and the ProSA score. The Ramachandran plot revealed that 92.1% of the amino acid residues were located in the most favorable region; this was complemented by an ERRAT score (overall quality factor) of 96.24 percent and a ProSA (Z score) of -9.24 percent. The Lipinski rule of five was used as an additional filter for screening molecules. The docking results showed values of binding affinity between -14.08 and -12.34 kcal/mol. The molecule C1 showed the highest docking value of -14.08 Kcal/mol with the maximum number of strong H-bonds by -NH of pyridine to amino acid Cys104 (4.22Å), -NH of indazole to Glu108 (4.72), and Glu70 to bridge H of -NH. These interactions are similar to Axitinib docking interactions like Glu70, Cys104, and Glu102. The docking studies revealed that pi-alkyl bonds are formed with unsubstituted pyridine, whereas important H-bonds are observed with different substitutions around -NH. Based on potential findings, we designed new molecules, and molecular docking studies were performed on the same protein along with ADMET studies. The designed molecules (M1-M4) also showed comparable docking results similar to Axitinib, along with a synthetic accessibility score of less than 4.5.

CONCLUSION

The docking method employed in this work opens up new possibilities for the design and synthesis of novel compounds that can act as VEGFR-2 tyrosine kinase inhibitors and treat cancer.

Potential Antidiabetic Activity of β-sitosterol from Zingiber roseum Rosc. via Modulation of Peroxisome Proliferator-activated Receptor Gamma (PPARγ)

AIM

To evaluate the antidiabetic potential of β-sitosterol from Zingiber roseum.

BACKGROUND

Diabetes mellitus is a cluster of metabolic disorders, and 90% of diabetic patients are affected with Type II diabetes (DM2). For the treatment of DM2, thiazolidinedione drugs (TZDs) were proposed, but recent studies have shown that TZDs have several detrimental effects, such as weight gain, kidney enlargement (hypertrophy), fluid retention, increased risk of bone fractures, and potential harm to the liver (hepatotoxicity). That is why a new molecule is needed to treat DM2.

OBJECTIVE

The current research aimed to assess the efficacy of β-Sitosterol from methanolic extract of Zingiber roseum in managing diabetes via PPARγ modulation.

METHODS

Zingiber roseum was extracted using methanol, and GC-MS was employed to analyze the extract. Through homology modeling, PPARγ structure was predicted. Molecular docking, MD simulation, free binding energies, QSAR, ADMET, and bioactivity and toxicity scores were all used during the in-depth computer-based research.

RESULTS

Clinically, agonists of synthetic thiazolidinedione (TZDs) have been used therapeutically to treat DM2, but these TZDs are associated with significant risks. Hence, GC-MS identified phytochemicals to search for a new PPAR-γ agonist. Based on the in-silico investigation, β-sitosterol was found to have a higher binding affinity (-8.9 kcal/mol) than standard drugs. MD simulations and MMGBSA analysis also demonstrated that β-sitosterol bound to the PPAR-γ active site stably.

CONCLUSION

It can be concluded that β-sitosterol from Z. roseum attenuates Type-II diabetes by modulating PPARγ activity.

Prediction of Phytochemicals for Their Potential to Inhibit New Delhi Metallo β-Lactamase (NDM-1)

The effectiveness of all antibiotics in the β-lactam group to cure bacterial infections has been impaired by the introduction of the New Delhi Metallo-β-lactamase (NDM-1) enzyme. Attempts have been made to discover a potent chemical as an inhibitor to this enzyme in order to restore the efficacy of antibiotics. However, it has been a challenging task to develop broad-spectrum inhibitors of metallo-β-lactamases. Lack of sequence homology across metallo-β-lactamases (MBLs), the rapidly evolving active site of the enzyme, and structural similarities between human enzymes and metallo-β-lactamases, are the primary causes for the difficulty in the development of these inhibitors. Therefore, it is imperative to concentrate on the discovery of an effective NDM-1 inhibitor. This study used various in silico approaches, including molecular docking and molecular dynamics simulations, to investigate the potential of phytochemicals to inhibit the NDM-1 enzyme. For this purpose, a library of about 59,000 phytochemicals was created from the literature and other databases, including FoodB, IMPPAT, and Phenol-Explorer. A physiochemical and pharmacokinetics analysis was performed to determine possible toxicity and mutagenicity of the ligands. Following the virtual screening, phytochemicals were assessed for their binding with NDM-1using docking scores, RMSD values, and other critical parameters. The docking score was determined by selecting the best conformation of the protein-ligand complex. Three phytochemicals, i.e., butein (polyphenol), monodemethylcurcumin (polyphenol), and rosmarinic acid (polyphenol) were identified as result of pharmacokinetics and molecular docking studies. Furthermore, molecular dynamics simulations were performed to determine structural stabilities of the protein-ligand complexes. Monodemethylcurcumin, butein, and rosmarinic acid were identified as potential inhibitors of NDM-1 based on their low RMSD, RMSF, hydrogen bond count, average Coulomb-Schrödinger interaction energy, and Lennard-Jones-Schrödinger interaction energy. The present investigation suggested that these phytochemicals might be promising candidates for future NDM-1 medication development to respond to antibiotic resistance.

In silico studies of alkaloids and their derivatives against N-acetyltransferase EIS protein from Mycobacterium tuberculosis

Antibiotic resistance against Mycobacterium tuberculosis (M.tb.) has been a significant cause of death worldwide. The Enhanced intracellular survival (EIS) protein of the bacteria is an acetyltransferase that multiacetylates aminoglycoside antibiotics, preventing them from binding to the bacterial ribosome. To overcome the EIS-mediated antibiotics resistance of M.tb., we compiled 888 alkaloids and derivatives from five different databases and virtually screened them against the EIS receptor. The compound library was filtered down to 87 compounds, which underwent additional analysis and filtration. Moreover, the top 15 most prominent phytocompounds were obtained after the drug-likeness prediction and ADMET screening. Out of 15, nine compounds confirmed the maximum number of hydrogen bond interactions and reliable binding energies during molecular docking. Additionally, the Molecular dynamics (MD) simulation of nine compounds showed the three most stable complexes, further verified by re-docking with mutated protein. The density functional theory (DFT) calculation was performed to identify the HOMO-LUMO energy gaps of the selected three potential compounds. Finally, our selected top lead compounds i.e., Alkaloid AQC2 (PubChem85634496), Nobilisitine A (ChEbi68116), and N-methylcheilanthifoline (ChEbi140673) demonstrated more favourable outcomes when compared with reference compounds (i.e., 39b and 2i) in all parameters used in this study. Therefore, we anticipate that our findings will help to explore and develop natural compound therapy against multi and extensively drug-resistant strains of M.tb.Communicated by Ramaswamy H. Sarma.

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

In Silico Identification of Potential Inhibitors of the SARS-CoV-2 Main Protease among a PubChem Database of Avian Infectious Bronchitis Virus 3CLPro Inhibitors

Remarkable structural homologies between the main proteases of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the avian infectious bronchitis virus (IBV) were revealed by comparative amino-acid sequence and 3D structural alignment. Assessing whether reported IBV 3CLPro inhibitors could also interact with SARS-CoV-2 has been undertaken in silico using a PubChem BioAssay database of 388 compounds active on the avian infectious bronchitis virus 3C-like protease. Docking studies of this database on the SARS-CoV-2 protease resulted in the identification of four covalent inhibitors targeting the catalytic cysteine residue and five non-covalent inhibitors for which the binding was further investigated by molecular dynamics (MD) simulations. Predictive ADMET calculations on the nine compounds suggest promising pharmacokinetic properties.

In Vivo Pharmacodynamics of Calophyllum soulattri as Antiobesity with In Silico Molecular Docking and ADME/Pharmacokinetic Prediction Studies

This study aims to determine the antiobesity activity of Calophyllum soulattri leaves extract (CSLE) on high fat diet-fed rats (HFD) and to predict the molecular docking and pharmacokinetics of selected compounds of Calophyllum soulattri to fat mass and obesity-associated protein (FTO). Daily body weight, organ, carcass fat (renal and anal), body mass index, total cholesterol, and total triglyceride levels were observed after CSLE was given orally for 50 days. Furthermore, body mass index of a CSLE dose of 50 mg/kgbw, 100 mg/kgbw and orlistat (120 mg/kgbw) group are 0.68, 0.57 and 0.52, respectively. The total body weight of the CLSE dose of 100 mg/kgbw group showed the lowest percentage change, followed by a CLSE dose of 50 mg/kgbw compared to the normal and positive control group. The carcass fat index of CSLE dose of 100 mg/kgbw was not significantly different from orlistat, which was in line with its total cholesterol level and triglyceride (p < 0.05). The binding affinity of selected compounds from Calophyllum soulattri (friedelin, caloxanthone B, macluraxanthone, stigmasterol, trapezifolixanthone, dombakinaxanthone, and brasixanthone B) to FTO are -8.27, -9.74, -8.48, -9.34, -8.85, -8.68 and -9.39 kcal/mol, which are better than that of orlistat at -4.80 kcal/mol. The molecular dynamics simulation showed that the interaction between Caloxanthone B compounds and obesity receptors was relatively stable. Lipinski's rule determined the absorption percentage of all compounds above 90% with good drug-likeness. The results showed the potential of CSLE as an antiobesity drug candidate.

Marine Alga Ulva fasciata-Derived Molecules for the Potential Treatment of SARS-CoV-2: An In Silico Approach

SARS-CoV-2 is the causative agent of the COVID-19 pandemic. This in silico study aimed to elucidate therapeutic efficacies against SARS-CoV-2 of phyco-compounds from the seaweed, Ulva fasciata. Twelve phyco-compounds were isolated and toxicity was analyzed by VEGA QSAR. Five compounds were found to be nonmutagenic, noncarcinogenic and nontoxic. Moreover, antiviral activity was evaluated by PASS. Binding affinities of five of these therapeutic compounds were predicted to possess probable biological activity. Fifteen SARS-CoV-2 target proteins were analyzed by the AutoDock Vina program for molecular docking binding energy analysis and the 6Y84 protein was determined to possess optimal binding affinities. The Desmond program from Schrödinger's suite was used to study high performance molecular dynamic simulation properties for 3,7,11,15-Tetramethyl-2-hexadecen-1-ol-6Y84 for better drug evaluation. The ligand with 6Y84 had stronger binding affinities (-5.9 kcal/mol) over two standard drugs, Chloroquine (-5.6 kcal/mol) and Interferon α-2b (-3.8 kcal/mol). Swiss ADME calculated physicochemical/lipophilicity/water solubility/pharmacokinetic properties for 3,7,11,15-Tetramethyl-2-hexadecen-1-ol, showing that this therapeutic agent may be effective against SARS-CoV-2.

Research into the mechanism of intervention of SanQi in endometriosis based on network pharmacology and molecular docking technology

BACKGROUND

By using network pharmacology and molecular docking technology, we have explored the mechanism of action of Sanqi in the treatment of endometriosis (EMS), in order to provide reference for clinical studies of Chinese medicine treatment of Ems and Chinese medicine pharmacology.

METHODS

There are 123 intersecting targets between the active ingredients of Sanqi and disease targets. In the Protein-Protein Interaction network, Jun proto-oncogene, AP-1 transcription factor subunit, tumor necrosis factor, interleukin 6, etc., are the core proteins. The top 20 genes ranked by degree have been analyzed according to the Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analysis, and 20 pathways have been identified.

RESULTS

On the Kyoto Encyclopedia of Genes and Genomes pathway, the most important part is the phosphatidylinositol 3'-kinase-Akt signaling pathway, and on the Gene Ontology pathway, it is the Heme binding. The top 3 targets docked to quercetin have a certain affinity when it is docked to their degree value. Among the chemical components of Sanqi, quercetin has the most targets, suggesting that it may play a major role in the treatment of EMS.

CONCLUSION

The results of molecular docking provide further evidence of the potential role of Sanqi for EMS. Overall, our study provides a new direction for the treatment of EMS and provides the basis for Sanqi as a drug for the treatment of EMS.

In silico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a substantial number of deaths around the world, making it a serious and pressing public health hazard. Phytochemicals could thus provide a rich source of potent and safer anti-SARS-CoV-2 drugs. The absence of approved treatments or vaccinations continues to be an issue, forcing the creation of new medicines. Computer-aided drug design has helped to speed up the drug research and development process by decreasing costs and time. Natural compounds like terpenoids, alkaloids, polyphenols, and flavonoid derivatives have a perfect impact against viral replication and facilitate future studies in novel drug discovery. This would be more effective if collaboration took place between governments, researchers, clinicians, and traditional medicine practitioners' safe and effective therapeutic research. Through a computational approach, this study aims to contribute to the development of effective treatment methods by examining the mechanisms relating to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). The in silico method has also been employed to determine the most effective drug among the mentioned compound and their aquatic, nonaquatic, and pharmacokinetics' data have been analyzed. The highest binding energy has been reported -11.4 kcal/mol against SARS-CoV-2 main protease (7MBG) in L05. Besides, all the ligands are non-carcinogenic, excluding L04, and have good water solubility and no AMES toxicity. The discovery of preclinical drug candidate molecules and the structural elucidation of pharmacological therapeutic targets have expedited both structure-based and ligand-based drug design. This review article will assist physicians and researchers in realizing the enormous potential of computer-aided drug design in the design and discovery of therapeutic molecules, and hence in the treatment of deadly diseases.

Chemical Compounds, Antioxidant Activities, and Inhibitory Activities Against Xanthine Oxidase of the Essential Oils From the Three Varieties of Sunflower (Helianthus annuus L.) Receptacles

Background/Aim: Essential oils of sunflower receptacles (SEOs) have antibacterial and antioxidant potential. However, the differences of biological activities from the different varieties of sunflowers have not been studied till now. The purpose of this study was to compare the differences of chemical compounds, antioxidant activities, and inhibitory activities against xanthine oxidase (XO) of SEOs from the three varieties of sunflowers including LD5009, SH363, and S606.

Methods: SEOs were extracted by using the optimal extraction conditions selected by response surface methodology (RSM). Chemical compounds of SEOs were identified from the three varieties of sunflowers by gas chromatography-mass spectrometry (GC-MS). Antioxidant activities of SEOs were detected by 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and iron ion reduction ability. Inhibitory activities of SEOs against XO were measured by using UV spectrophotometer. XO inhibitors were selected from the main chemical compounds of SEOs by the high-throughput selections and molecular simulation docking.

Results: The extraction yields of SEOs from LD5009, SH363, and S606 were 0.176, 0.319, and 0.580%, respectively. A total of 101 chemical compounds of SEOs were identified from the three varieties of sunflowers. In addition, the results of inhibitory activities against XO showed that SEOs can reduce uric acid significantly. Eupatoriochromene may be the most important chemical compounds of SEOs for reducing uric acid. The results of antioxidant activities and inhibitory activities against XO showed that SEOs of LD5009 had the strongest antioxidant and XO inhibitory activities. The Pearson correlation coefficient (r > 0.95) showed that γ-terpinene, (E)-citral, and L-Bornyl acetate were highly correlated with the antioxidant activities and XO inhibitory ability.

Conclusion: SEOs had antioxidant activities and XO inhibitory ability. It would provide more scientific information for utilization and selection of varieties of sunflowers, which would increase the food quality of sunflowers and incomes of farmers.

Identification of Natural Inhibitors Against SARS-CoV-2 Drugable Targets Using Molecular Docking, Molecular Dynamics Simulation, and MM-PBSA Approach

The present study explores the SARS-CoV-2 drugable target inhibition efficacy of phytochemicals from Indian medicinal plants using molecular docking, molecular dynamics (MD) simulation, and MM-PBSA analysis. A total of 130 phytochemicals were screened against SARS-CoV-2 Spike (S)-protein, RNA-dependent RNA polymerase (RdRp), and Main protease (M^pro). Result of molecular docking showed that Isoquercetin potentially binds with the active site/protein binding site of the Spike, RdRP, and Mpro targets with a docking score of -8.22, -6.86, and -9.73 kcal/mole, respectively. Further, MS 3, 7-Hydroxyaloin B, 10-Hydroxyaloin A, showed -9.57, -7.07, -8.57 kcal/mole docking score against Spike, RdRP, and M^pro targets respectively. The MD simulation was performed to study the favorable confirmation and energetically stable complex formation ability of Isoquercetin and 10-Hydroxyaloin A phytochemicals in M^pro-unbound/ligand bound/standard inhibitor bound system. The parameters such as RMSD, RMSF, Rg, SASA, Hydrogen-bond formation, energy landscape, principal component analysis showed that the lead phytochemicals form stable and energetically stabilized complex with the target protein. Further, MM-PBSA analysis was performed to compare the Gibbs free energy of the M^pro-ligand bound and standard inhibitor bound complexes. The analysis revealed that the His-41, Cys145, Met49, and Leu27 amino acid residues were majorly responsible for the lower free energy of the complex. Drug likeness and physiochemical properties of the test compounds showed satisfactory results. Taken together, the study concludes that that the Isoquercetin and 10-Hydroxyaloin A phytochemical possess significant efficacy to bind SARS-Cov-2 M^pro active site. The study necessitates further in vitro and in vivo experimental validation of these lead phytochemicals to assess their anti-SARS-CoV-2 potential.

Covalent Antiviral Agents

Nowadays, many viral infections have emerged and are taking a huge toll on human lives globally. Meanwhile, viral resistance to current drugs has drastically increased. Hence, there is a pressing need to design potent broad-spectrum antiviral agents to treat a variety of viral infections and overcome viral resistance. Covalent inhibitors have the potential to achieve both goals owing to their biochemical efficiency, prolonged duration of action, and the capability to inhibit shallow, solvent-exposed substrate-binding domains. In this chapter, we review the structures, activities, and inhibition mechanisms of covalent inhibitors against severe acute respiratory syndrome coronavirus 2, dengue virus, enterovirus, hepatitis C virus, human immunodeficiency virus, and influenza viruses. We also discuss the application of in silico study in covalent inhibitor design.

Phytochemicals present in Indian ginseng possess potential to inhibit SARS-CoV-2 virulence: A molecular docking and MD simulation study

Coronaviruses are deadly and contagious pathogens that affects people in different ways. Researchers have increased their efforts in the development of antiviral agents against coronavirus targeting M^pro protein (main protease) as an effective drug target. The present study explores the inhibitory potential of characteristic and non-characteristic Withania somnifera (Indian ginseng) phytochemicals (n ≈ 100) against SARS-Cov-2 M^pro protein. Molecular docking studies revealed that certain W. somnifera compounds exhibit superior binding potential (−6.16 to −12.27 kcal/mol) compared to the standard inhibitors (−2.55 to −6.16 kcal/mol) including nelfinavir and lopinavir. The non-characteristic compounds (quercetin-3-rutinoside-7-glucoside, rutin and isochlorogenic acid B) exhibited higher inhibitory potential in comparison to characteristic W. somnifera compounds withanolide and withanone. Molecular dynamics (MD) simulation studies of the complex for 100 ns confirm favorable and stable binding of the lead molecule. The MMPBSA calculation of the last 10 ns of the protein-ligand complex trajectory exhibited stable binding of quercetin-3-rutinoside-7-glucoside at the active site of SARS-Cov-2 M^pro. Taken together, the study demonstrates that the non-characteristic compounds present in W. somnifera possess enhanced potential to bind SARS-Cov-2 M^pro active site. We further recommend in vitro and in vivo experimentation to validate the anti-SARS-CoV-2 potential of these lead molecules.

Targeting the Autophagy Specific Lipid Kinase VPS34 for Cancer Treatment: An Integrative Repurposing Strategy

The impact of autophagy on cancer treatment and its corresponding responsiveness has galvanized the scientific community to develop novel inhibitors for cancer treatment. Importantly, the discovery of inhibitors that targets the early phase of autophagy was identified as a beneficial choice. Despite the number of research in recent years, screening of the DrugBank repository (9591 molecules) for the Vacuolar protein sorting 34 (VPS34) has not been reported earlier. Therefore, the present study was designed to identify potential VPS34 antagonists using integrated pharmacophore strategies. Primarily, an energy-based pharmacophore and receptor cavity-based analysis yielded five (DHRRR) and seven featured (AADDHRR) pharmacophore hypotheses respectively, which were utilized for the database screening process. The glide score, the binding free energy, pharmacokinetics and pharmacodynamics properties were examined to narrow down the screened compounds. This analysis yielded a hit molecule, DB03916 that exhibited a better docking score, higher binding affinity and better drug-like properties in contrast to the reference compound that suffers from a toxicity property. Importantly, the result was validated using a 50 ns molecular dynamics simulation study. Overall, we conclude that the identified hit molecule DB03916 is believed to serve as a prospective antagonist against VPS34 for cancer treatment.

Discovery of Potential Inhibitors for RNA-Dependent RNA Polymerase of Norovirus: Virtual Screening, and Molecular Dynamics

Noroviruses are non-enveloped viruses with a positive-sense single-stranded RNA (ssRNA) genome belonging to the genus Norovirus, from the family Caliciviridae, which are accountable for acute gastroenteritis in humans. The Norovirus genus is subdivided into seven genogroups, i.e., (GI-GVII); among these, the genogroup II and genotype 4 (GII.4) strains caused global outbreaks of human norovirus (HuNov) disease. The viral genome comprises three open reading frames (ORFs). ORF1 encodes the nonstructural polyprotein that is cleaved into six nonstructural proteins, which include 3C-like cysteine protease (3CLpro) and a viral RNA-dependent RNA polymerase. ORF2 and ORF3 encode the proteins VP1 and VP2. The RNA-dependent RNA polymerase (RdRp) from noroviruses is one of the multipurpose enzymes of RNA viruses vital for replicating and transcribing the viral genome, making the virally encoded enzyme one of the critical targets for the development of novel anti-norovirus agents. In the quest for a new antiviral agent that could combat HuNov, high throughput virtual screening (HTVS), combined with e-pharmacophore screening, was applied to screen compounds from the PubChem database. CMX521 molecule was selected as a prototype for a similarity search in the PubChem online database. Molecular dynamics simulations were employed to identify different compounds that may inhibit HuNov. The results predicted that compound CID-57930781 and CID-44396095 formed stable complexes with MNV-RdRp within 50 ns; hence, they may signify as promising human norovirus inhibitors.

Deep neural network modeling based virtual screening and prediction of potential inhibitors for renin protein

Renin enzyme plays an essential role in the Renin-Angiotensin System (RAS), and it is involved in the pathogenesis of hypertension and several other cardiovascular diseases (CVDs). Inhibition of renin is an effective way to intervene with the pathogenesis of these diseases. Docking-based virtual screening, 3D-Quantitative Structure-Activity Relationship (3D-QSAR), and structure-based drug design are the most frequently used strategies towards discovering novel inhibitors targeting renin. In this study, we have developed a 2D fingerprint-based Deep Neural Network (DNN) classifier for virtual screening and a DNN-QSAR model for biological activity prediction. The resulting hits from the DNN-QSAR model were then subjected to the molecular docking to identify further top hits. Molecular Dynamics (MD) simulation was conducted to get a better insight into the binding mode of identified hits. We have discovered six compounds from the Maybridge chemical database with the predicted IC₅₀ values ranging from 24.2 nM to 83.6 nM. To the best of our knowledge, this is the first study that used a cascaded DNN model to identify potential lead compounds for the inhibition of renin target. Through the results presented in this study, we provide evidence of the DNN method being a useful approach to identify new chemical entities/novel lead compounds that may overcome the limitation of existing conventional strategies used in drug discovery research.Communicated by Ramaswamy H. Sarma.

Identification of Putative Non-Substrate-Based XT-I Inhibitors by Natural Product Library Screening

Fibroproliferative diseases are characterized by excessive accumulation of extracellular matrix (ECM) components leading to organ dysfunction. This process is characterized by an increase in myofibroblast content and enzyme activity of xylosyltransferase-I (XT-I), the initial enzyme in proteoglycan (PG) biosynthesis. Therefore, the inhibition of XT-I could be a promising treatment for fibrosis. We used a natural product-inspired compound library to identify non-substrate-based inhibitors of human XT-I by UPLC-MS/MS. We combined this cell-free approach with virtual and molecular biological analyses to confirm and prioritize the inhibitory potential of the compounds identified. The characterization for compound potency in TGF-β1-driven XYLT1 transcription regulation in primary dermal human fibroblasts (key cells in ECM remodeling) was addressed by gene expression analysis. Consequently, we identified amphotericin B and celastrol as new non-substrate-based XT-I protein inhibitors. Their XT-I inhibitory effects were mediated by an uncompetitive or a competitive inhibition mode, respectively. Both compounds reduced the cellular XYLT1 expression level and XT-I activity. We showed that these cellular inhibitor-mediated changes involve the TGF-β and microRNA-21 signaling pathway. The results of our study provide a strong rationale for the further optimization and future usage of the XT-I inhibitors identified as promising therapeutic agents of fibroproliferative diseases.

Identification of potential natural inhibitors of SARS-CoV2 main protease by molecular docking and simulation studies

Coronaviruses are contagious pathogens primarily responsible for respiratory and intestinal infections. Research efforts to develop antiviral agents against coronavirus demonstrated the main protease (Mpro) protein may represent effective drug target. X-ray crystallographic structure of the SARS-CoV2 Mpro protein demonstrated the significance of Glu166, Cys141, and His41 residues involved in protein dimerization and its catalytic function. We performed in silico screening of compounds from Curcuma longa L. (Zingiberaceae family) against Mpro protein inhibition. Employing a combination of molecular docking, scoring functions, and molecular dynamics simulations, 267 compounds were screened by docking on Mpro crystallographic structure. Docking score and interaction profile analysis exhibited strong binding on the Mpro catalytic domain with compounds C1 (1E,6E)-1,2,6,7-tetrahydroxy-1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene-3,5-dione) and C2 (4Z,6E)‐1,5‐dihydroxy‐1,7‐bis(4‐hydroxyphenyl)hepta‐4,6‐dien‐3‐one as lead agents. Compound C1 and C2 showed minimum binding score (–9.08 and –8.07 kcal/mole) against Mpro protein in comparison to shikonin and lopinavir (≈ −5.4 kcal/mole) a standard Mpro inhibitor. Furthermore, principal component analysis, free energy landscape and protein-ligand energy calculation studies revealed that these two compounds strongly bind to the catalytic core of the Mpro protein with higher efficacy than lopinavir, a standard antiretroviral of the protease inhibitor class. Taken together, this structure based optimization has provided lead on two natural Mpro inhibitors for further testing and development as therapeutics against human coronavirus.

Communicated by Ramaswamy H. Sarma

Assessment of malathion toxicity on cytophysiological activity, DNA damage and antioxidant enzymes in root of Allium cepa model

The current study was emphasized to assess the effect of malathion on root system (cell division and kinetics of the root elongation) and stress related parameters in Allium cepa L. The roots were exposed to different concentrations (0.05, 0.13, 0.26, 0.39 and 0.52 g/L) of malathion for different treatment periods (4, 8 and 18 h). The results revealed that malathion application affected the growth rate and cell division in root tips. The root elongation kinetics were impaired at 0.13 to 0.52 g/L concentrations. Reduction in tissue water content (TWC) indicated the limited osmotic adjustment due to membrane damage. Further, a decrease in sucrose content was observed in contrast to the accumulation of proline (upto 0.39 g/L). Moreover, malathion exposure elevated the levels of lipid peroxidation followed by changes in antioxidant enzymes status. The activities of ascorbate peroxidase (APX) and glutathione reductase (GR) were down-regulated whereas the activities of catalase (CAT), glutathione-S-transferase (GST) and superoxide dismutase (SOD) were up-regulated except in 0.52 g/L malathion. The molecular docking study of malathion with CAT, GST, SOD, APX and GR also supported of above results for their activity. All these physiological responses varied with increasing malathion concentration and duration of treatment. The single cell gel electrophoresis results showed that all concentrations of malathion induced DNA damage in root cells. The findings depicted that malathion application induces cytotoxic and phytotoxic effects mediated through oxidative stress and subsequent injuries.

Drugena: A Fully Automated Immunoinformatics Platform for the Design of Antibody-Drug Conjugates Against Neurodegenerative Diseases

Antibodies are proteins that are the first line of defense in the adaptive immune response of vertebrates. Thereby, they are involved in a multitude of biochemical mechanisms and clinical manifestations with significant medical interest, such as autoimmunity, the regulation of infection, and cancer. An emerging field in antibody science that is of huge medicinal interest is the development of novel antibody-interacting drugs. Such entities are the antibody-drug conjugates (ADCs), which are a new type of targeted therapy, which consist of an antibody linked to a payload drug. Overall, the underlying principle of ADCs is the discerning delivery of a drug to a target, hoping to increase the potency of the original drug. Drugena suite is a pioneering platform that employs state-of-the-art computational biology methods in the fight against neurodegenerative diseases using ADCs. Drugena encompasses an up-to-date structural database of specialized antibodies for neurological disorders and the NCI database with over 96 million entities for the in silico development of ADCs. The pipeline of the Drugena suite has been divided into several steps and modules that are closely related with a synergistic fashion under a user-friendly graphical user interface.

In Silico Mapping of Essential Residues in the Catalytic Domain of PDE5 Responsible for Stabilization of Its Commercial Inhibitors

Phosphodiesterase type 5 (PDE5) is an important enzyme associated with the hydrolysis of cyclic guanosine monophosphate (cGMP) to guanosine monophosphate (GMP). Due to the relevant role of second messenger cGMP as a mediator in many physiological processes, efforts have been converged to find a safe pharmacological approach, seeking a specific, selective and potent inhibitor of the PDE5 enzyme. There are five commercial drugs with potential for clinical use: tadalafil, sildenafil, avanafil, udenafil and vardenafil. Here, we applied molecular modeling to obtain different profiles of protein–ligand interactions by adopting distinct PDE5 structures, specifically PDBid:1XOZ and two extracted from molecular dynamics (MD) simulations. The results generated by molecular docking showed several possibilities for inhibitor interactions with the catalytic pocket. Tadalafil, sildenafil and vardenafil were clearly stabilized by Gln817 via a well-oriented hydrogen bond. Another set of different interactions, such as polar, hydrophobic, π-stacking, metal–ligand and electrostatic, were responsible for accommodating avanafil and udenafil. All of the ligands are discussed in detail with consideration of the distinct protein structures, and a profile of the probability of residue–ligand contact is suggested, with the most frequently observed being: Tyr612, His613, Ser661, Thr723, Asp724, Asp764, Leu765, Val782 and Phe786. The molecular interactions displayed herein confirm findings achieved by previous authors and also present new contacts. In addition, the discussion can help researchers obtain a molecular basis for planning new selective PDE5 inhibitors, as well as explain an inhibitor’s experimental assays by considering the specific interactions occurring at the catalytic site.

Development and Testing of Druglike Screening Libraries

Although significant advances in experimental high throughput screening (HTS) have been made for drug lead identification, in silico virtual screening (VS) is indispensable owing to its unique advantage over experimental HTS, target-focused, cheap, and efficient, albeit its disadvantage of producing false positive hits. For both experimental HTS and VS, the quality of screening libraries is crucial and determines the outcome of those studies. In this paper, we first reviewed the recent progress on screening library construction. We realized the urgent need for compiling high-quality screening libraries in drug discovery. Then we compiled a set of screening libraries from about 20 million druglike ZINC molecules by running fingerprint-based similarity searches against known drug molecules. Lastly, the screening libraries were objectively evaluated using 5847 external actives covering more than 2000 drug targets. The result of the assessment is very encouraging. For example, with the Tanimoto coefficient being set to 0.75, 36% of external actives were retrieved and the enrichment factor was 13. Additionally, drug target family specific screening libraries were also constructed and evaluated. The druglike screening libraries are available for download from https://mulan.pharmacy.pitt.edu .

Herbonanoceuticals: A Novel Beginning in Drug Discovery and Therapeutics

The Indian pharmaceutical industry is the world’s second largest industry (by volume) that develops products and market drugs licensed for use as medications. Medicines manufactured in the modern era are associated with major controversies such as non–target specificity, resistance, repeated administration, immune rejection, and other adverse effects on the body. Thus, there is a great need to find drugs that do not raise the aforementioned issues. Nature is an excellent hub providing a diverse range of phytoconstituents that open the way to phototherapeutics, which need a scientific path to deliver the active elements in a supported way to increase patient compliance and reduce the need for repeated administration. To discover a novel phytochemical as a lead compound for a therapeutic purpose is a real challenge. In former times, drug discovery was a complex process, as it took several years to find a lead compound for use against a particular disease. Nowadays, however, virtual screening methods have been developed, which are target specific, time consuming, and cost effective. To avoid increased and repeated administration of a drug, nanosized drug delivery systems for herbal drugs have been developed to enhance the activity and overcome problems associated with synthetic medicines. This review summarizes three main fields: drug discovery, docking for drug design, and last—but not least—drug delivery systems. Nowadays, nanobased drug delivery systems are in demand for delivery of herbal medicines used for therapeutic purposes. Herbonanoceuticals—herbal drugs of a nanosize—have better remedial value and fewer detrimental effects than modern medicines. Therefore, herbonanoceuticals can be a boon in the field of therapeutics.

Prediction of Compound Profiling Matrices Using Machine Learning

Screening of compound libraries against panels of targets yields profiling matrices. Such matrices typically contain structurally diverse screening compounds, large numbers of inactives, and small numbers of hits per assay. As such, they represent interesting and challenging test cases for computational screening and activity predictions. In this work, modeling of large compound profiling matrices was attempted that were extracted from publicly available screening data. Different machine learning methods including deep learning were compared and different prediction strategies explored. Prediction accuracy varied for assays with different numbers of active compounds, and alternative machine learning approaches often produced comparable results. Deep learning did not further increase the prediction accuracy of standard methods such as random forests or support vector machines. Target-based random forest models were prioritized and yielded successful predictions of active compounds for many assays.

A novel identification approach for discovery of 5-HydroxyTriptamine 2A antagonists: combination of 2D/3D similarity screening, molecular docking and molecular dynamics

5-HydroxyTriptamine 2A antagonists are potential targets for treatment of various cerebrovascular and cardiovascular disorders. In this study, we have developed and performed a unique screening pipeline for filtering ZINC database compounds on the basis of similarities to known antagonists to determine novel small molecule antagonists of 5-HydroxyTriptamine 2A. The screening pipeline is based on 2D similarity, 3D dissimilarity and a combination of 2D/3D similarity. The shortlisted compounds were docked to a 5-HydroxyTriptamine 2A homology-based model, and complexes with low binding energies (287 complexes) were selected for molecular dynamics (MD) simulations in a lipid bilayer. The MD simulations of the shortlisted compounds in complex with 5-HydroxyTriptamine 2A confirmed the stability of the complexes and revealed novel interaction insights. The receptor residues S239, N343, S242, S159, Y370 and D155 predominantly participate in hydrogen bonding. π-π stacking is observed in F339, F340, F234, W151 and W336, whereas hydrophobic interactions are observed amongst V156, F339, F234, V362, V366, F340, V235, I152 and W151. The known and potential antagonists shortlisted by us have similar overlapping molecular interaction patterns. The 287 potential 5-HydroxyTriptamine 2A antagonists may be experimentally verified.

Road Map for the Structure-Based Design of Selective Covalent HCV NS3/4A Protease Inhibitors

Over the last 2 decades, covalent inhibitors have gained much popularity and is living up to its reputation as a powerful tool in drug discovery. Covalent inhibitors possess many significant advantages including increased biochemical efficiency, prolonged duration and the ability to target shallow, solvent exposed substrate-binding domains. However, rapidly mounting concerns over the potential toxicity, highly reactive nature and general lack of selectivity have negatively impacted covalent inhibitor development. Recently, a great deal of emphasis by the pharmaceutical industry has been placed toward the development of novel approaches to alleviate the major challenges experienced through covalent inhibition. This has unexpectedly led to the emergence of "selective" covalent inhibitors. The purpose of this review is not only to provide an overview from literature but to introduce a technical guidance as to how to initiate a systematic "road map" for the design of selective covalent inhibitors which we believe may assist in the design and development of optimized potential selective covalent HCV NS3/4A viral protease inhibitors.

Structural Analysis of Sortase A Inhibitors

Bacterial sortases are cysteine transpeptidases that regulate the covalent linkage of several surface protein virulence factors in Gram-positive bacteria. Virulence factors play significant roles in adhesion, invasion of host tissues, biofilm formation and immune evasion, mediating the bacterial pathogenesis and infectivity. Therefore, sortases are emerging as important targets for the design of new anti-infective agents. We employed a computational study, based on structure derived descriptors and molecular fingerprints, in order to develop simple classification methods which could allow predicting low active or high active SrtA inhibitors. Our results indicate that a highly active SrtA inhibitor has a molecular weight ranging between 180 and 600, contains one up to four nitrogen atoms, up to three oxygen atoms and under 18 hydrogen atoms. Also the hydrogen acceptor number and the molecular flexibility, as assessed by the number of rotatable bounds, have emerged as the most relevant descriptors for SrtA affinity. The Bemis-Murcko scaffolding revealed favoured scaffolds as containing at least two ring structures bonded directly or merged in a condensed cycle. This data represent a valuable tool for identifying new potent SrtA inhibitors, potential anti-virulence agents targeted against Gram-positive bacteria, including multiresistant strains.

Discovery of potential ALK inhibitors by virtual screening approach

Crizotinib is an anticancer drug used for the treatment of non-small cell lung cancer. Evidences available suggest that there is a development of an acquired resistance against crizotinib action due to the emergence of several mutations in the ALK gene. It is therefore necessary to develop potent anti-cancer drugs for the treatment of crizotinib resistance non-small cell lung cancer types. In the present study, a novel class of lead molecule was identified using virtual screening, molecular docking and molecular dynamic approach. The virtual screening analysis was done using PubChem database by employing crizotinib as query and the data reduction was carried out by using molecular docking techniques. The bioavailability of the lead compounds was examined with the help of Lipinski rule of five. The screened lead molecules were analyzed for toxicity profiles, drug-likeness and other physico-chemical properties of drugs by OSIRIS program. Finally, molecular dynamics simulation was also performed to validate the binding property of the lead compound. Our analysis clearly indicates that CID 11562217, a nitrile containing compound (pyrazole-substituted aminoheteroaryl), could be the potential ALK inhibitor certainly helpful to overcome the drug resistance in non-small cell lung cancer.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

INTRODUCTION

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.

AREAS COVERED

In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.

EXPERT OPINION

The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

New design of nucleotide excision repair (NER) inhibitors for combination cancer therapy

Many cancer chemotherapy agents act by targeting the DNA of cancer cells, causing substantial damage within their genome and causing them to undergo apoptosis. An effective DNA repair pathway in cancer cells can act in a reverse way by removing these drug-induced DNA lesions, allowing cancer cells to survive, grow and proliferate. In this context, DNA repair inhibitors opened a new avenue in cancer treatment, by blocking the DNA repair mechanisms from removing the chemotherapy-mediated DNA damage. In particular, the nucleotide excision repair (NER) involves more than thirty protein-protein interactions and removes DNA adducts caused by platinum-based chemotherapy. The excision repair cross-complementation group 1 (ERCC1)-xeroderma pigmentosum, complementation group A (XPA) protein (XPA-ERCC1) complex seems to be one of the most promising targets in this pathway. ERCC1 is over expressed in cancer cells and the only known cellular function so far for XPA is to recruit ERCC1 to the damaged point. Here, we build upon our recent advances in identifying inhibitors for this interaction and continue our efforts to rationally design more effective and potent regulators for the NER pathway. We employed in silico drug design techniques to: (1) identify compounds similar to the recently discovered inhibitors, but more effective at inhibiting the XPA-ERCC1 interactions, and (2) identify different scaffolds to develop novel lead compounds. Two known inhibitor structures have been used as starting points for two ligand/structure-hybrid virtual screening approaches. The findings described here form a milestone in discovering novel inhibitors for the NER pathway aiming at improving the efficacy of current platinum-based therapy, by modulating the XPA-ERCC1 interaction.

Compound annotation with real time cellular activity profiles to improve drug discovery

INTRODUCTION

In the past decade, a range of innovative strategies have been developed to improve the productivity of pharmaceutical research and development. In particular, compound annotation, combined with informatics, has provided unprecedented opportunities for drug discovery.

AREAS COVERED

In this review, a literature search from 2000 to 2015 was conducted to provide an overview of the compound annotation approaches currently used in drug discovery. Based on this, a framework related to a compound annotation approach using real-time cellular activity profiles for probe, drug, and biology discovery is proposed.

EXPERT OPINION

Compound annotation with chemical structure, drug-like properties, bioactivities, genome-wide effects, clinical phenotypes, and textural abstracts has received significant attention in early drug discovery. However, these annotations are mostly associated with endpoint results. Advances in assay techniques have made it possible to obtain real-time cellular activity profiles of drug molecules under different phenotypes, so it is possible to generate compound annotation with real-time cellular activity profiles. Combining compound annotation with informatics, such as similarity analysis, presents a good opportunity to improve the rate of discovery of novel drugs and probes, and enhance our understanding of the underlying biology.

Structural Interactions of Curcumin Biotransformed Molecules with the N-Terminal Residues of Cytotoxic-Associated Gene A Protein Provide Insights into Suppression of Oncogenic Activities

Curcumin as a natural product has drawn considerable attention in recent years for its multiple pharmacological activities against various diseases, but more studies are required to understand the curcumin pharmacological action considering its low bioavailability. Though numerous reasons contribute to the low bioavailability of curcumin, one of the important reasons is associated with biotransformation of curcumin through either conjugation or reduction depending on curcumin administration route. The orally administered curcumin (CUR) is metabolised into curcumin glucuronidase (CUR-GLR) and curcumin sulphate by conjugation, whereas dihydroxycurcumin, tetrahydrocurcumin, and hexahydrocurcumin (HHC) are formed by reduction after intraperitoneal administration of curcumin. The main aim of the current study was to investigate the pharmacological properties of curcumin and its biotransformed molecules and its inhibitory potential against CagA (cytotoxic-associated gene A) oncoprotein of Helicobacter pylori. All lead molecules followed the Lipinski's five rules for biological activities, except CUR-GLR, whereas druglikeness scores were obtained for all molecules. Subsequently, molecular docking was employed to analyse the binding affinity of molecules with CagA. The docking studies revealed that CUR-GLR has highest binding affinity with CagA, whereas less interactive affinity was observed in HHC. From the virtual screening and docking studies, the current study suggests that the biotransformation of curcumin through conjugation has more potential for inhibition of oncogenic activities of CagA+ H. pylori than reduction.

Target enhanced 2D similarity search by using explicit biological activity annotations and profiles

BACKGROUND

The enriched biological activity information of compounds in large and freely-accessible chemical databases like the PubChem Bioassay Database has become a powerful research resource for the scientific research community. Currently, 2D fingerprint based conventional similarity search (CSS) is the most common widely used approach for database screening, but it does not typically incorporate the relative importance of fingerprint bits to biological activity.

RESULTS

In this study, a large-scale similarity search investigation has been carried out on 208 well-defined compound activity classes extracted from PubChem Bioassay Database. An analysis was performed to compare the search performance of three types of 2D similarity search approaches: 2D fingerprint based conventional similarity search approach (CSS), iterative similarity search approach with multiple active compounds as references (ISS), and fingerprint based iterative similarity search with classification (ISC), which can be regarded as the combination of iterative similarity search with active references and a reversed iterative similarity search with inactive references. Compared to the search results returned by CSS, ISS improves recall but not precision. Although ISC causes the false rejection of active hits, it improves the precision with statistical significance, and outperforms both ISS and CSS. In a second part of this study, we introduce the profile concept into the three types of searches. We find that the profile based non-iterative search can significantly improve the search performance by increasing the recall rate. We also find that profile based ISS (PBISS) and profile based ISC (PBISC) significantly decreases ISS search time without sacrificing search performance.

CONCLUSIONS

On the basis of our large-scale investigation directed against a wide spectrum of pharmaceutical targets, we conclude that ISC and ISS searches perform better than 2D fingerprint similarity searching and that profile based versions of these algorithms do nearly as well in less time. We also suggest that the profile version of the iterative similarity searches are both better performing and potentially quicker than the standard algorithm.

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

Prediction of cytochrome P450 mediated metabolism

Cytochrome P450 enzymes (CYPs) form one of the most important enzyme families involved in the metabolism of xenobiotics. CYPs comprise many isoforms, which catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be formed. However, it is often hard to rationalize what metabolites these enzymes generate. In recent years, many different in silico approaches have been developed to predict binding or regioselective product formation for the different CYP isoforms. These comprise ligand-based methods that are trained on experimental CYP data and structure-based methods that consider how the substrate is oriented in the active site or/and how reactive the part of the substrate that is accessible to the heme group is. We will review key aspects for various approaches that are available to predict binding and site of metabolism (SOM), what outcome can be expected from the predictions, and how they could potentially be improved.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.