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Recently emerged SARS-CoV-2 is the cause of the ongoing outbreak of COVID-19. It is responsible for the deaths of millions of people and has caused global economic and social disruption. The numbers of COVID-19 cases are increasing exponentially across the world. Control of this pandemic disease is challenging because there is no effective drug or vaccine available against this virus and this situation demands an urgent need for the development of anti-SARS-CoV-2 potential medicines. In this regard, the main protease (Mpro) has emerged as an essential drug target as it plays a vital role in virus replication and transcription. In this research, we have identified two novel potent inhibitors of the Mpro (PubChem3408741 and PubChem4167619) from PubChem database by pharmacophore-based high-throughput virtual screening. The molecular docking, toxicity, and pharmacophore analysis indicate that these compounds may act as potential anti-viral candidates. The molecular dynamic simulation along with the binding free energy calculation by MMPBSA showed that these compounds bind to Mpro enzyme with high stability over 50 ns. Our results showed that two compounds: PubChem3408741 and PubChem4167619 had the binding free energy of − 94.02 kJ mol−1 and − 122.75 kJ mol−1, respectively, as compared to reference X77 (− 76.48 kJ mol−1). Based on our work’s findings, we propose that these compounds can be considered as lead molecules for targeting Mpro enzyme and they can be potential SARS-CoV-2 inhibitors. These inhibitors could be tested in vitro and explored for effective drug development against COVID-19. Graphic abstract Electronic supplementary material The online version of this article (10.1007/s11030-020-10148-5) contains supplementary material, which is available to authorized users.

Background: Superantigens of Staphylococcus aureus namely enterotoxin A, exfoliative toxin A, and Toxic shock syndrome toxin-1 cause detrimental effects on the cells of the immune system. Methods: In this work, the toxins were downloaded from the Protein DataBank database and energies were minimized using KoBaMIN server. Forty flavonoids compounds were identified by pubchem compound database through extensive literature study and their 3D structures were obtained by submitting SMILES to CORINA tool. Based on Lipinski’s rule of five, the molecules were filtered that resulted in 27 compounds. Molecular docking was performed for identifying the binding and interaction sites of flavonoids with the toxins using Autodock 4. Results and Conclusion: The docked complexes were then subjected to molecular dynamics simulation using Gromacs. The analysis revealed the stability of the complexes as indicated by three hydrogen bonds formed during the simulation time period of 20 ns.

Background:Coronary heart disease generally occurs due to cholesterol accumulation in the walls of the heart arteries. Statins are the most widely used drugs which work by inhibiting the active site of 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGCR) enzyme that is responsible for cholesterol synthesis. A series of atorvastatin analogs with HMGCR inhibition activity have been synthesized experimentally which would be expensive and time-consuming.Methods:In the present study, we employed both the QSAR model and chemical similarity search for identifying novel HMGCR inhibitors for heart-related diseases. To implement this, a 2D QSAR model was developed by correlating the structural properties to their biological activity of a series of atorvastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of atorvastatin analogs was performed by using PubChem database search.Results and Discussion:The three-descriptor model of charge (GATS1p), connectivity (SCH-7) and distance (VE1_D) of the molecules is obtained for HMGCR inhibition with the statistical values of R2= 0.67, RMSEtr= 0.33, R2 ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained by chemical similarity search and the inhibition activities of the compounds were predicted using QSAR model, which were close in the range of experimentally observed threshold.Conclusion:The present study suggests that the QSAR model and chemical similarity search could be used in combination for identification of novel compounds with activity by in silico with less computation and effort.

The novel SARS-CoV-2 is the etiological agent causing the Coronavirus disease 2019 (COVID-19), which continues to become an inevitable pandemic outbreak. Over a short span of time, the structures of therapeutic target proteins for SARS-CoV-2 were identified based on the homology modelled structure of similar virus, SARS-CoV that transmitted rapidly in 2003. Since the outset of the disease, the research community has been looking for a potential drug lead. Out of all the known resolved structures related to SARS-CoV-2; 3-chymotrypsin (3 C) like protease (3CLpro) is considered as an attractive anti-viral drug compound on the grounds of its role in viral replication and probable non-interactive competency to bind to any viral host protein. To the best of our knowledge, till date only one compound has been identified and tested in-vitro as a potent inhibitor of 3CLpro protein, addressed as N3 (PubChem Compound CID: 6323191) and is known to bind irreversibly to 3CLpro suppressing its activity. Using computational approach, we intend to identify a probable natural fungal metabolite to interact and inhibit 3CLpro. Here after performing docking and molecular dynamics of various small molecules derived as a secondary metabolite from fungi, we propose Flaviolin as potent inhibitor of 3CLpro of novel Coronavirus SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Moringa oleifera is also known as “Miracle tree”, due to its multiple uses and adaptability. Because of nutritive and pharmacological values, it is widely cultivated across the world. M. oleifera leaves are rich source of minerals, vitamins and many health beneficial secondary metabolites, and possess significant anti-diabetic potential. Consequently, Insilco study could be noteworthy to expand effective anti-diabetic drugs from this plant. Present study was designed to find out the best bioactive compounds of M. oleifera as a potential therapeutic agent against diabetes mellitus through In-silico method. For this, structures of phytochemicals were extracted from PubChem and docked to mutated protein from PBD. Afterwards, datasets were prepared for ligand based pharmacophore and their pharmacophoric features were generated from LigandScout. Finally five phytochemicals viz. anthraquinone, 2-phenylchromenylium (Anthocyanins), hemlock tannin, sitogluside (glycoside) and A-phenolic steroid were selected, which exhibited effective binding within the active binding pocket of the targeted protein. Ligand based pharmacophore model showed the key features i.e. HBD, HBA, aromatic ring, hydrophobic, positively ionizable surface essential for receptor binding. Our findings suggest that screened phytochemicals present in M. oleifera can be used as potential therapeutic drug candidates to treat diabetes mellitus.

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is one of the top five most visited chemistry web sites in the world, with more than five million unique users per month (as of March 2020). Many of these users are educators, undergraduate students, and graduate students at academic institutions. Therefore, PubChem has a great potential as an online resource for chemical education. This paper describes the PubChem Periodic Table and Element pages, which were recently introduced to celebrate the 150th anniversary of the periodic table. These services help users navigate the abundant chemical element data available within PubChem, while providing a convenient entry point to explore additional chemical content, such as biological activities and health and safety data available in PubChem Compound pages for specific elements and their isotopes. The PubChem Periodic Table and Element pages are also available as widgets, which enable web developers to display PubChem’s element data on web pages they design. The elemental data can be downloaded in common file formats and imported into data analysis programs (e.g., spreadsheet software, like Microsoft Excel and Google Sheets, and computer scripts, such as python and R). Overall, the PubChem Periodic Table and Element pages improve access to chemical element data from authoritative sources.

As scientific community worldwide is publishing a huge number of research articles in various fields; it is necessary to distinguish between databases that are efficient and objective for literature searches. This review offers information on the important points of the database. None of the databases are complete and perfect, but they complement each other. If a library can only afford one, choice must be based on the priorities of institutional needs. The benefits that databases can provide in the preparation of the literature review for developing future studies and dissemination of research are discussed. This paper provides an overview of the most frequently used free chemistry databases such as PubChem, Crystallography Open Database, PubMed, ZINC, ChemSpider, and Google Scholar. It also gives a brief description of three major commercial databases such as Scopus, Web of Science, and SciFinder. Thus, substance and citation databases that covers almost all areas of chemistry, has become an invaluable tool in bibliometric analysis.

Respiratory disease caused by a novel coronavirus, COVID-19, has been labeled a pandemic by the World Health Organization. Very little is known about the infection mechanism for this virus. More importantly, there are no drugs or vaccines that can cure or prevent a person from getting COVID-19. In this study, the binding affinity of 2692 protease inhibitor compounds that are known in the protein data bank, are calculated against the main protease of the novel coronavirus with docking and molecular dynamics (MD). Both the docking and MD methods predict the macrocyclic tissue factor–factor VIIa (PubChem ID: 118098670) inhibitor to bind strongly with the main protease with a binding affinity of −10.6 and −10.0 kcal/mol, respectively. The TF-FVIIa inhibitors are known to prevent the coagulation of blood and have antiviral activity as shown in the case of SARS coronavirus. Two more inhibitors, phenyltriazolinones (PubChem ID: 104161460) and allosteric HCV NS5B polymerase thumb pocket 2 (PubChem ID: 163632044) have shown antiviral activity and also have high affinity towards the main protease of COVID-19. Furthermore, these inhibitors interact with the catalytic dyad in the active site of the COVID-19 main protease that is especially important in viral replication. The calculated theoretical dissociation constants of the proposed COVID-19 inhibitors are found to be very similar to the experimental dissociation constant values of similar protease-inhibitor systems. Communicated by Ramaswamy H. Sarma

Background:: Urolithiasis is the process of forming stones in the kidney, bladder, and/or urinary tract. It has been reported that kidney stones are the third most common disorder among urinary diseases. At present, surgical procedures and Extracorporeal Shock Wave Lithotripsy (ESWL) are commonly employed for the treatment of Urolithiasis. The major drawback of these procedures is the recurrence of stones. Methods: This study aimed to identify potential natural inhibitors against human Serum Albumin (SA) from the plant Scoparia Dulsis for Urolithiasis. As protein-ligand interactions play a key role in structure- based drug design, this study screened 26 compounds from Scoparia Dulsis and investigated their binding affinity against SA by using molecular docking. The three dimensional (3D) structure of SA was retrieved from Protein Data Bank (PDB) and docked with PubChem structures of 26 compounds using PyRX docking tool through Autodock Vina. Moreover, a 3D similarity search on the PubChem database was performed to find the analogs of best scored compound and docking studies were performed. Drug-likeness studies were made using Swiss ADME and Lipinski’s rule of five was performed for the compounds to evaluate their anti-urolithiatic activity. Results: The results showed that citrusin c (Eugenyl beta-D-glucopyranoside) exhibited best binding energy of -8.1 kcal/mol with SA followed by aphidicolin, apigenin, luteolin and scutellarein. Two compounds (PubChem CID 46186820, PubChem CID 21579141) analogous to citrusin c were selected based on the lowest binding energy. Conclusion: This study, therefore, reveals that these compounds could be promising candidates for further evaluation for Urolithiasis prevention or management.

The emergence of the coronavirus disease-2019 pandemic has led to an outbreak in the world. The SARS-CoV-2 is seventh and latest in coronavirus family with unique exonucleases for repairing any mismatches in newly transcribed genetic material. Therefore, drugs with novel additional mechanisms are required to simultaneously target and eliminate the virus. Thus, a newly deciphered N protein is taken as a target that belongs to SARS-CoV-2. They play a vital role in RNA transcription, viral replication and new virion formation. This study used virtual screening, molecular modeling and docking of the 8987 ligands from Asinex and PubChem databases against this novel target protein. Three hotspot sites having DScore ≥1 (Site 1, Site 2 and Site 3) for ligand binding were selected. Subsequently, high throughput screening, standard precision and extra precision docking process and molecular dynamics concluded three best drugs from two libraries. Two antiviral moieties from Asinex databases (5817 and 6799) have docking scores of −10.29 and −10.156; along with their respective free binding energies (ΔG bind) of −51.96 and −64.36 on Site 3. The third drug, Zidovudine, is from PubChem database with docking scores of −9.75 with its binding free energies (ΔG bind) of −59.43 on Site 3. The RMSD and RMSF were calculated for all the three drugs through molecular dynamics simulation studies for 50 ns. Zidovudine shows a very stable interaction with fluctuation starting at 2.4 Å on 2 ns and remained stable at 3 Å from 13 to 50 ns. Thus, paving the way for further biological validation as a potential treatment. Communicated by Ramaswamy H. Sarma

Flavonoids are by far the most dominant class of phenolic compounds isolated from Morus alba leaves (MAL). Other classes of compounds are benzofurans, phenolic acids, alkaloids, coumarins, chalcones and stilbenes. Major flavonoids are kuwanons, moracinflavans, moragrols and morkotins. Other major compounds include moracins (benzofurans), caffeoylquinic acids (phenolic acids) and morachalcones (chalcones). Research on the anticancer properties of MAL entailed in vitro and in vivo cytotoxicity of extracts or isolated compounds. Flavonoids, benzofurans, chalcones and alkaloids are classes of compounds from MAL that have been found to be cytotoxic towards human cancer cell lines. Further studies on the phytochemistry and anticancer of MAL are suggested. Sources of information were PubMed, PubMed Central, ScienceDirect, Google, Google Scholar, J-Stage, PubChem and China National Knowledge Infrastructure.

The present study investigates anti-diabetic capacity of compounds enclosed in the R. tuberosa L. root extracts by molecular simulation approach to examine the potential of those compounds acting as alpha-glucosidase inhibitors. Compounds chosen were cirsimarin, cirsimaritin, and sorbifolin; quercetin was used for the reference. Those compounds were downloaded from PubChem database, and human alpha-glucosidase 3D structure was obtained from Protein Data Bank. The protein was docked to the flavonoid compounds using HEX 8.0 software and visualized using Discovery Studio 4.1. The interactions of cirsimarin, cirsimaritin, sorbifolin, and quercetin on alpha-glucosidase showed similar binding patterns. They interacted with the active sites of the enzyme, causing inhibition on enzyme activity. The interactions between proteins and ligands were mostly through formation of hydrogen bonds and Van der Waals forces. The binding energy of cirsimarin cirsimaritin, sorbifolin, and quercetin to alpha glucosidase were comparable at -323.3, -279.4, -256.8, and -241.5 cal/mol, respectively. These confirm that compounds contained in the extracts of R. tuberosa L have capacity to be used as inhibitor for alpha glucosidase.

Bioscientists reading papers or patents strive to discern the key relationships reported within a document “D“ where a bioactivity “A” with a quantitative result “R” (e.g., an IC50) is reported for chemical structure “C” that modulates (e.g., inhibits) a protein target “P”. A useful shorthand for this connectivity thus becomes DARCP. The problem at the core of this article is that the community has spent millions effectively burying these relationships in PDFs over many decades but must now spend millions more trying to get them back out. The key imperative for this is to increase the flow into structured open databases. The positive impacts will include expanded data mining opportunities for drug discovery and chemical biology. Over the last decade commercial sources have manually extracted DARCP from ≈300,000 documents encompassing ≈7 million compounds interacting with ≈10,000 targets. Over a similar time, the Guide to Pharmacology, BindingDB and ChEMBL have carried out analogues DARCP extractions. Although their expert-curated numbers are lower (i.e., ≈2 million compounds against ≈3700 human proteins), these open sources have the great advantage of being merged within PubChem. Parallel efforts have focused on the extraction of document-to-compound (D-C-only) connectivity. In the absence of molecular mechanism of action (mmoa) annotation, this is of less value but can be automatically extracted. This has been significantly accomplished for patents, (e.g., by IBM, SureChEMBL and WIPO) for over 30 million compounds in PubChem. These have recently been joined by 1.4 million D-C submissions from three major chemistry publishers. In addition, both the European and US PubMed Central portals now add chemistry look-ups from abstracts and full-text papers. However, the fully automated extraction of DARCLP has not yet been achieved. This stands in contrast to the ability of biocurators to discern these relationships in minutes. Unfortunately, no journals have yet instigated a flow of author-specified DARCP directly into open databases. Progress may come from trends such as open science, open access (OA), findable, accessible, interoperable and reusable (FAIR), resource description framework (RDF) and WikiData. However, we will need to await the technical applicability in respect to DARCP capture to see if this opens up connectivity.

The Artificial Insemination (AI) aims to develop the potential of the cattle reproduction comprehensively. The success of AI results is highly dependent on the level of livestock fertility. Therefore, improv the quality and quantity of semen could be done by hormone induction. However, hormone prices are expensive. This problem could be overcome using alternative way with the natural compound containing chrysin which might has the same function. The purpose of the study was analyze the potential of chrysin compound from Bungli plant (Oroxylum indicum) as fertilizer of males through bioinformatics approach. Methods performed through data search by webserver with the following stages: (1) Pubchem, (2) Swiss (3) Target Prediction, (4) Uniprot, (5) Protein Data Bank and (6) docking software using PyRx, PyMOL and Discovery Studio. The results showed that the chrysin compound interact with carbonic anhydrase (CA) expressed from the testes of the cattle, through van der Waals and Pi-Alkyl bonds. Chrysin is effective as CA ligand inducers based on affinity bonds (-8.4 kcal/mole) and more negatively than flavonol as a control compound with binding affinity (-6.2 kcal/mol). This suggests that chrysin is an effective compound as a potential drug for improved livestock fertility.

In this work, cytidine and fifteen of its derivatives have been examined to detect their Methyltransferase (MTF) enzyme inhibitory activity. 3D models of the ligands and MTF were extracted from PubChem and Protein Data Bank (PDB), respectively. All ligand structures were first optimized to obtain their minimum energy structures. Molecular features were obtained for the optimized structures. The molecular docking process was performed for all of the ligands versus MTF enzyme to obtain the interacting ligand-receptor complexes. The results indicated that, the derivatives of cytidine revealed better enzyme inhibitory activity compared with that of the original structures. Moreover, chemical modifications showed different impacts on the molecular features and enzyme activities. Therefore, it is important to choose the type of modifications to the desired chemical structure. Among the investigated derivatives, D4: Galocitabine showed the best properties to be proposed as the best inhibitors, and it is a great candidate for further investigations.

The advent of computational methods for efficient prediction of the druglikeness of small molecules and their ever-burgeoning applications in the fields of medicinal chemistry and drug industries have been a profound scientific development, since only a few amounts of the small molecule libraries were identified as approvable drugs. In this study, a deep belief network was utilized to construct a druglikeness classification model. For this purpose, small molecules and approved drugs from the ZINC database were selected for the unsupervised pre-training step and supervised training step. Various binary fingerprints such as Macc 166 bit, PubChem 881 bit, and Morgan 2048 bit as data features were investigated. The report revealed that using an unsupervised pre-training phase can lead to a good performance model and generalizability capability. Accuracy, precision, and recall of the model for Macc features were 97%, 96%, and 99%, respectively. For more consideration about the generalizability of the model, the external data by expression and investigational drugs in drug banks as drug data and randomly selected data from the ZINC database as non-drug were created. The results confirmed the good performance and generalizability capability of the model. Also, the outcomes depicted that a large proportion of misclassified non-drug small molecules ascertain the bioavailability conditions and could be investigated as a drug in the future. Furthermore, our model attempted to tap potential opportunities as a drug filter in drug discovery.

Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.

Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.

Oil was extracted from Frankincense (Boswellia Sacra) by the soxhlet extraction method and various bioactive compounds were identified using gas chromatography (GC).These compounds can be developed as active pharmacophores.The present study involves the extraction and identification of bioactive compounds and their in-vitro study on the interaction of these compounds to target proteins. Frankincense oil collected from Boswelia Sacra species was subjected to Soxhlet extraction using Hexane solvent and essential oil (EO) was separated using vacuum distillation. Chemical profiling of essential oil was done using GC-MS. Various biological databases like PubChem, Protein Data Bank and software like Argus Lab, Rasmol were used to retrieve and analyze the structural and molecular interactions of bioactive compounds from Frankincense oil with receptor proteins. The target protein structure was retrieved from Protein data bank ligand structures that were downloaded from Pubchem, which was visualized using Rasmol Software. Protein-ligand interaction was studied using Argus Lab software by docking simulations and various docking poses were analyzed.The energy values of docking conformations were analyzed for obtaining the best docking pose & score.

Caspase-3 is a biochemical marker for cell apoptosis. Several studies focused on exploring caspase inhibitor potential in natural compounds. Hence, in this study investigated the anthocyanins as anti-apoptotic potential activity through caspase-3 using molecular docking. Six types of anthocyanin were retrieved from PubChem database and caspase-3 protein was downloaded from Protein Data Bank. Anthocyanins and caspase-3 protein were docked using HEX 8.0 program and visualized using Discovery Studio 4.1 software. The interaction among cyanidin-3-O-glucoside, delphinidin-3-O-glucoside, pelargonidin-3-O-glucoside, peonidin-3-O-glucoside and petunidin-3-O-glucoside showed similar binding pattern on caspase-3 protein. All of them bind to BIR2 region and allosteric site of caspase-3, which are a crucial site for apoptosis regulation. Interestingly, malvidin-3-O-glucoside also interacted with caspase-3 in BIR1, BIR2 and BIR3 regions. In addition, anthocyanins-caspase-3 complex showed low energy and demonstrated several hydrogen bonds, hydrophobic interactions and van der Waals interactions, which indicated stable interaction. This study implies that all anthocyanins have potential as inhibitor of caspase-3 protein and might have potential as anti-apoptosis. Further in-vitro and in-vivo studies are need to confirm this experimental.

Graphical abstract, Highlights • Three CHM components with the potential against COVID-19 are identified using TCMSP. • SPR assay result shows good binding affinity of puerarin and quercetin to ACE2. • Puerarin and quercetin impairs the binding of viral S-protein to ACE2 receptor. • Quercetin can also directly bind to S-protein to exert an viral neutralizing effect. • Results from this study propose a prompt application of puerarin on COVID-19 patients., The outbreak of COVID-19 raises an urgent need for the therapeutics to contain the emerging pandemic. However, no effective treatment has been found for SARS-CoV-2 infection to date. Here, we identified puerarin (PubChem CID: 5281807), quercetin (PubChem CID: 5280343) and kaempferol (PubChem CID: 5280863) as potential compounds with binding activity to ACE2 by using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Molecular docking analysis showed that puerarin and quercetin exhibit good binding affinity to ACE2, which was validated by surface plasmon resonance (SPR) assay. Furthermore, SPR-based competition assay revealed that puerarin and quercetin could significantly affect the binding of viral S-protein to ACE2 receptor. Notably, quercetin could also bind to the RBD domain of S-protein, suggesting not only a receptor blocking, but also a virus neutralizing effect of quercetin on SARS-CoV-2. The results from network pharmacology and bioinformatics analysis support a view that quercetin is involved in host immunomodulation, which further renders it a promising candidate against COVID-19. Moreover, given that puerarin is already an existing drug, results from this study not only provide insight into its action mechanism, but also propose a prompt application of it on COVID-19 patients for assessing its clinical feasibility.

Two-way liquid organic hydrogen carriers (LOHC) – organic molecules that store hydrogen as reversible chemical bonds – is an emerging concept for on-demand storage and transportation of hydrogen (and thereby energy). Given the large chemical universe, a plethora of potential LOHCs exist, however, the optimal candidate depends on satisfying a variety of constraints on physicochemical and thermochemical properties. Computational high-throughput screening of a subspace of this universe can, in principle, reveal several LOHC candidates which can then be experimentally verified; however, to achieve this, the hydrogen rich and lean forms of the LOHC pair have to be simultaneously identified based on a plausible connecting chemical pathway. Here, using a combination of data-driven molecular property models and a cheminformatics-based reaction network generation tool, viz. RING, we develop a novel computational workflow to identify promising LOHC pairs (i.e. hydrogen-rich and hydrogen-lean forms) and the dehydrogenation pathways connecting them. Starting from over 1 million small (containing less than 14 heavy atoms) molecules in the PubChem database as seed, we applied this framework as proof of concept to identify several LOHC pairs that have promising properties in terms of melting point, boiling point, dehydrogenation enthalpy, hydrogen storage capacity, and synthetic accessibility; we further analyze the thermochemistry of dehydrogenation pathways of the top five candidates. We finally show that this screening provides a rich dataset that can be harnessed via supervised learning algorithms to infer descriptive features that determine if a molecule is a good LOHC candidate. We posit that the proposed workflow can be used to scalably analyze a much larger molecular space and multiple classes of dehydrogenation chemistries to discover novel LOHC pairs.

Gallic acid (PubChem CID: 370) and quercetin (PubChem CID: 5280343) are major phenolic compounds in many mangrove plants that have been related to health cure. In the present study, the active fractions namely gallic acid (1) and quercetin (2) were isolated from the methanolic extract of leaves of Ceriops tagal in a Tropical mangrove ecosystem of Andaman and Nicobar Island (ANI), India. The chemical structures were determined by spectroscopic analysis: Fourier-Transform Infrared spectroscopy (FT-IR), 1H, 13C Nuclear Magnetic Resonance (NMR) spectroscopy, and High-resolution mass spectrometry (HRMS). The anticancer activity of isolated compounds (1) and (2) were evaluated by in vitro assays against two human cancer cell lines namely, HeLa (Cervical) and MDA-MB231 (Breast) cancer cells revealed that IC50 values of gallic acid (HeLa: 4.179197 �� 0.45 ��g/ml; MDA-MB231: 80.0427 �� 0.19 ��g/ml at 24 h) and quercetin (HeLa: 99.914 �� 0.18 ��g/ml; MDA-MB231: 18.288382 �� 0.12 ��g/ml at 24 h), respectively. Antioxidant properties of gallic acid (1) and quercetin (2) are found to be IC50 value of 0.77 �� 0.41 ��g/ml and 1.897 �� 0.81 ��g/ml, respectively. Molecular docking results explained that gallic acid (1) and quercetin (2) showed estimated binding free energy (��G) of ���5.4 and ���6.9 kcal/mol towards drug target Bcl-B protein, respectively. The estimated inhibition constant (Ki) for these two molecules are 110 and 8.75 ��M, respectively. The MD simulation additionally supported that quercetin molecule is significantly improved the structural stability of Bcl-B protein. The present study provides key insights about the importance of polyphenols, and thus leads to open the therapeutic route for anti-cancer drug discovery process. Communicated by Ramaswamy H. Sarma

The diversity of hundreds of thousands of potential organic pollutants and the lack of (publicly available) information about many of them is a huge challenge for environmental sciences, engineering, and regulation. Suspect screening based on high-resolution liquid chromatography-mass spectrometry (LC-HRMS) has enormous potential to help characterize the presence of these chemicals in our environment, enabling the detection of known and newly emerging pollutants, as well as their potential transformation products (TPs). Here, suspect list creation (focusing on pesticides relevant for Luxembourg, incorporating data sources in 4 languages) was coupled to an automated retrieval of related TPs from PubChem based on high confidence suspect hits, to screen for pesticides and their TPs in Luxembourgish river samples. A computational workflow was established to combine LC-HRMS analysis and pre-screening of the suspects (including automated quality control steps), with spectral annotation to determine which pesticides and, in a second step, their related TPs may be present in the samples. The data analysis with Shinyscreen (https://gitlab.lcsb.uni.lu/eci/shinyscreen/), an open source software developed in house, coupled with custom-made scripts, revealed the presence of 162 potential pesticide masses and 96 potential TP masses in the samples. Further identification of these mass matches was performed using the open source approach MetFrag (https://msbi.ipb-halle.de/MetFrag/). Eventual target analysis of 36 suspects resulted in 31 pesticides and TPs confirmed at Level-1 (highest confidence), and five pesticides and TPs not confirmed due to different retention times. Spatio-temporal analysis of the results showed that TPs and pesticides followed similar trends, with a maximum number of potential detections in July. The highest detections were in the rivers Alzette and Mess and the lowest in the Sûre and Eisch. This study (a) added pesticides, classification information and related TPs into the open domain, (b) developed automated open source retrieval methods - both enhancing FAIRness (Findability, Accessibility, Interoperability and Reusability) of the data and methods; and (c) will directly support "L'Administration de la Gestion de l'Eau" on further monitoring steps in Luxembourg.

Background:In recent years, there has been an exponential increase in the global burden of cancer which has been associated with several factors including environmental influence, aging, diet, infectious agents, hormonal imbalance and chronic inflammation, among others. Cancerous cells utilize more glucose for its proliferation and survival than normal cells. Thus, the regulation of glucose consumption of cancerous cells through the inhibition of glucose transporter-4-protein (GLUT4) encoded by solute carrier family-2-member-4-gene (Slc2a4) by selected phytochemicals from Solanum xanthocarpum may serve as a new therapeutic candidate for the treatment of cancer.Methods:The seven identified potential inhibitors of GLUT4 from Solanum xanthocarpum were retrieved from PubChem database. Examination of their drug-likeness, toxicity prediction and molecular docking studies of these compounds with GLUT4 were carried out using online tools such as Molinspiration, PreADMET V.2.0 and Patchdock server.Results:The findings revealed that, five out of the seven compounds fulfil oral drugability of Lipinski’s rule of five (RO5) while two slightly meet the criteria of RO5. Conversely, five of the compounds are predicted to be mutagen while the remaining two are predicted to be safe for the body. Additionally, stigmasterol glucoside has higher binding-affinity (7590) with GLUT4 when compared to doxorubicin (6600) the control.Conclusion:These findings suggest that stigmasterol glucoside from Solanum xanthocarpum could be a promising therapeutic agent with better therapeutic efficacy than doxorubicin in the treatment of cancer via the inhibition of GLUT4.

Identifying drug-target interactions plays an important role in discovering drugs. Identifying, finding, and preparing drug molecule targets is the key for modern drug discovery. However, potential drug-target interactions are usually determined experimental approaches (in vivo and in vitro). Experimental approaches are expensive, require a lot of manpower, and the data are complex, making it difficult to use these methods effectively. For these reasons, the importance of simulation-based methods (in-silico) has increased and computational methods have started to be used more actively. In addition, more computational methods need to be developed to validate the interactions between drugs and their targets. In order to predict and validate the interactions between drugs and their targets by computational methods, both drugs and targets need to be mapped and to be classified with artificial intelligence techniques. As it is known, targets consist of proteins and protein sequences consist of letters. Furthermore, drug compounds are expressed in molecular codes. It is not possible to determine the interactions between drugs and their targets by computational methods without any pre-processing (mapping). The performance of the DTI (Drug-Target Interaction) prediction process varies according to the protein mapping and artificial intelligence approaches selected thus, it is important to choose the right methods in such applications. There are a number of protein mapping techniques and artificial intelligence algorithms in the literature. In this study, prediction of drug-target interactions carried out for COVID-19 disease by using certain protein mapping techniques and a deep learning. The proposed method consists of 5 stages. In the first stage, drug-target interactions were obtained from the DrugBank database. In the second stage, mapping of drug compounds and target proteins was made. While PubChem fingerprinting method was used for the mapping of drug compounds, target proteins were mapped with 6 different methods; Meiler parameters, Atchley factors, PAM250, BLOSUM62, Miyazawa energies and Micheletti potentials. In the third stage, the mapped drug compounds and the mapped target proteins were combined and a one-dimensional feature space was obtained. In the fourth stage, the one-dimensional feature that was generated before was classified with the LSTM (Long-Short Term Memory) deep learning model and the prediction was performed. In the last stage, the performance of the protein mapping methods was determined and compared with accuracy, precision, recall, f1-score, and ROC (Receiver Operating Characteristic) evaluation matrices. When the application results were examined, it was seen that all protein mapping techniques performed above 85%. The best accuracy and ROC scores were obtained from Atchley factors and Meiler parameters. With Atchley factors, an average of 92% accuracy and 98% ROC were obtained. With the Meiler parameters, the ROC value did not change, but the accuracy value was measured as 91%. Afterwards, it was observed that Micheletti potentials and Miyazawa energies showed the second best performance. On average, 90 and 91% accuracy values were obtained, respectively. ROC values were calculated to be close to each other and 98% ROC value was obtained for Micheletti potentials, while this ratio decreased to 96% with Miyazawa energies. BLOSUM62 and PAM250 protein mapping methods were more ineffective than other methods. While BLOSM62 showed an average accuracy of 87%, PAM250 predicted drug-target interactions an average of 91% accuracy. While the ROC value of the BLOSUM62 method was 89%, this rate increased in PAM250 and a ROC value of 92% was obtained. Contributions obtained by the end of the study can be expressed as follows; with this study for the first time, drug-target interactions of COVID-19 were predicted by protein mapping techniques. In addition, the most effective protein mapping method among protein mapping techniques was determined. It was demonstrated that the selected protein mapping techniques are important in determining drug-target interactions. Additionally, it has been observed that computational-based methods can be at least as effective as experimental approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Ethnopharmacological Relevance:Plectranthus genus (Lamiaceae family) contain several species with acknowledged ethnopharmacological uses, such as, for gastrointestinal and respiratory-related problems, due to their anti-inflammatory, antibacterial and antifungal properties. The bioactivity of isolated medicinal compounds from this genus justifies the increased interest in recent times for species of Plectranthus, placing them in the spotlight for natural product drug development.Aim of the study: To the best of our knowledge, this is the first review on the biological activities of Plectranthus ecklonii Benth. As such, the aim of this review was three-fold: 1) to summarize the chemical compounds isolated from P. ecklonii; 2) to collate the biological activities and mechanisms of action of these compounds from in vitro studies; and 3) to evaluate the documented uses and potential applications of this species, in order to postulate on the direction of pharmaceutical uses of this species.Materials and methods: An extensive database retrieval was performed using the electronic databases Web of Science, PubMed, Google Scholar and ScienceDirect. The search criteria consisted of the keywords “Plectranthus ecklonii”, “Plectranthus ecklonii + review”, “Plectranthus ecklonii + diterpenes” or “Plectranthus ecklonii + abietanes”, “ecklonii + parviflorone D”, searched individually and as combinations. Eligibility criteria were set out and titles in English, Portuguese and Spanish were reviewed, with all references included dating from 1970 to 2021. A total of 169 papers were selected and included. Chemical structures were drawn using ChemDraw 20.0, CID numbers were searched in PubChem and the PRISMA diagram was created using PowerPoint 2012.Results: To date, a total of 28 compounds have been isolated from P. ecklonii, including diterpenes, triterpenes, flavonoids, and hydroxycinnamic acids. Most focused on the antimicrobial action of its constituents, although compounds have demonstrated other bioactivities, namely antioxidant, anti-inflammatory and antitumor. The most recent studies emphasize the diterpenoids, particularly parviflorone D, with the help of nanotechnology.Conclusions: The widespread ethnobotanical and traditional uses of P. ecklonii can be scientifically justified by a range of biological activities, demonstrated by isolated secondary metabolites. These bioactivities showcase the potential of this species in the development of economically important active pharmaceutical ingredients, particularly in anticancer therapy.

Bioinformatic presentation

Chemical named entity recognition (NER) is a significant step for many downstream applications like entity linking for the chemical text-mining pipeline. However, the identification of chemical entities in a biomedical text is a challenging task due to the diverse morphology of chemical entities and the different types of chemical nomenclature. In this work, we describe our approach that was submitted for BioCreative version 7 challenge Track 2, focusing on the ‘Chemical Identification’ task for identifying chemical entities and entity linking, using MeSH. For this purpose, we have applied a two-stage approach as follows (a) usage of fine-tuned BioBERT for identification of chemical entities (b) semantic approximate search in MeSH and PubChem databases for entity linking. There was some friction between the two approaches, as our rule-based approach did not harmonise optimally with partially recognized words forwarded by the BERT component. For our future work, we aim to resolve the issue of the artefacts arising from BERT tokenizers and develop joint learning of chemical named entity recognition and entity linking using pre-trained transformer-based models and compare their performance with our preliminary approach. Next, we will improve the efficiency of our approximate search in reference databases during entity linking. This task is non-trivial as it entails determining similarity scores of large sets of trees with respect to a query tree. Ideally, this will enable flexible parametrization and rule selection for the entity linking search.

Various methods (Hartree–Fock methods, semi-empirical methods, Density Functional Theory, Molecular Mechanics) used to optimize a molecule structure feature the same basic approach but differ in the mathematical approximations used. The geometry optimization procedure calculates the energy at an initial geometry of a molecule and then proceeds to search a new geometry with a lower energy. Using the 3D structures collected from the PubChem database, 20 amino acid geometry optimization calculations were performed with several methods. The purpose of the study was to analyze these methods (39) to find the relationship between them and to determine which to use under different circumstances. Cluster analysis and principal component analysis were performed to evaluate the similarities between the different methods. The results after the analysis can classified into three main groups and can be selected accordingly to solve different types of problems.

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of the drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50

Hepatitis C virus (HCV) inhibitors are essential in the treatment of human norovirus (HuNoV). This study aimed to map out HCV NS5B RNA-dependent RNA polymerase inhibitors that could potentially be responsible for the inhibitory activity of HuNoV RdRp. It is necessary to develop robust machine learning and in silico methods to predict HuNoV RdRp compounds. In this study, Naïve Bayesian and random forest models were built to categorize norovirus RdRp inhibitors from the non-inhibitors using their molecular descriptors and PubChem fingerprints. The best model observed had accuracy, specificity, and sensitivity values of 98.40%, 97.62%, and 97.62%, respectively. Meanwhile, an external test set was used to validate model performance before applicability to the screened HCV compounds database. As a result, 775 compounds were predicted as NoV RdRp inhibitors. The pharmacokinetics calculations were used to filter out the inhibitors that lack drug-likeness properties. Molecular docking and molecular dynamics simulation investigated the inhibitors’ binding modes and residues critical for the HuNoV RdRp receptor. The most active compound, CHEMBL167790, closely binds to the binding pocket of the RdRp enzyme and depicted stable binding with RMSD 0.8–3.2 Å, and the RMSF profile peak was between 1.0–4.0 Å, and the conformational fluctuations were at 450–460 residues. Moreover, the dynamic residue cross-correlation plot also showed the pairwise correlation between the binding residues 300–510 of the HuNoV RdRp receptor and CHEMBL167790. The principal component analysis depicted the enhanced movement of protein atoms. Moreover, additional residues such as Glu510 and Asn505 interacted with CHEMBL167790 via water bridge and established H-bond interactions after the simulation. http://zinc15.docking.org/substances/ZINC000013589565.

The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism, mitochondrial biogenesis and autophagy, and cellular growth. In many diseases—such as metabolic syndrome, obesity, diabetes, and also cancer—activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.

Objectives The current pandemic caused by Severe Acute Respiratory Syndrome Corona-Virus 2 (SARS-CoV-2) has become a global health menace with significant morbidity and mortality besides huge socioeconomic implications. Despite the approval of few vaccines for the prevention of the disease, the discovery of safe and effective countermeasures especially from natural sources is of paramount importance, as the number of cases continues escalating. Arq Ajīb has long been used for various diseases and its ingredients have been reported for antiviral, antimicrobial, antipyretic, anti-inflammatory, antioxidant activities. The present study investigates the inhibitory effect of phytocompound of Arq Ajīb on potential drug targets of SARS-CoV-2. Methods The structures of phytocompounds present in Arq Ajīb were retrieved from PubChem database and some were illustrated using Marvin Sketch. SARS-CoV-2 S glycoprotein (PDB ID: 6LZG) and 3CLpro (PDB ID: 7BQY) were selected as the target protein. Dock Prep module in UCSF Chimera software was used for receptor structure processing. AutoDock Vina was used to calculate the binding affinities between the protein and ligands and to predict most promising compounds with best scores. Results Molecular docking results predicted that the phytocompounds of Arq Ajīb had good binding affinity and interaction with S glycoprotein and 3CLpro. Quercetin and Isorhoifolin from Mentha arvensis were identified as promising candidates with the potential to interact with 3CLpro and spike glycoprotein and inhibit the viral replication and its entry into the host. Conclusions Arq Ajīb may prove valuable for developing novel therapeutic candidate for COVID-19; however, it has to be substantiated further with in-vitro and in-vivo studies.

Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of β1- and β2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.

BackgroundCocaine use disorder (CUD) and associated psychosis are major public health issues worldwide, along with high relapse outcome and limited treatment options. Exploring the molecular mechanisms underlying cocaine-induced psychosis (CIP) could supply integrated insights for understanding the pathogenic mechanism and potential novel therapeutic targets.AimsThe aim of the study was to explore common alterations of CUD-schizophrenia-target genes and identify core risk genes contributing to CIP through data mining and network pharmacology approach.MethodsTarget genes of CUD were obtained from GeneCards, Comparative Toxicogenomics Database, Swiss Target Prediction platform and PubChem. Schizophrenia-related target genes were derived from DisGeNET, GeneCards, MalaCards and Online Mendelian Inheritance in Man databases. Then, the overlap genes of these two sets were regarded as risk genes contributing to CIP. Based on these CUD-schizophrenia-target genes, functional annotation and pathway analysis were performed using the clusterProfiler package in R. Protein–protein interaction network construction and module detection were performed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Gene expression datasets GSE54839 and GSE93577 were applied for data validation and diagnostic capacity evaluation of interested hub genes.ResultsA total of 165 CUD-schizophrenia-target genes were obtained. These genes were mainly contributing to chemical synaptic transmission, neuropeptide hormone activity, postsynaptic membrane and neuroactive ligand–receptor interaction pathway. Network analysis and validation analysis indicated that BDNF might serve as an important risk gene in mediating CIP.ConclusionsThis study generates a holistic view of CIP and provides a basis for the identification of potential CUD-schizophrenia-target genes involved in the development of CIP. The abnormal expression of BDNF would be a candidate therapeutic target underlying the pathogenesis of CUD and associated CIP.

V-domain Ig suppressor of T cell activation (VISTA) is an immune checkpoint and is a type I transmembrane protein. VISTA is linked to immunotherapy resistance, and it is a potential immune therapeutic target, especially for triple-negative breast cancer. It expresses at a high concentration in regulatory T cells and myeloid-derived suppressor cells, and its functional blockade is found to delay tumor growth. A useful medicinal plant database for drug designing (MPD3), which is a collection of phytochemicals from diverse plant families, was employed in virtual screening against VISTA to prioritize natural inhibitors against VISTA. Three compounds, Paratocarpin K (PubChem ID: 14187087), 3-(1H-Indol-3-yl)-2-(trimethylazaniumyl)propanoate (PubChem ID: 3861164), and 2-[(5-Benzyl-4-ethyl-1,2,4-triazol-3-yl)sulfanylmethyl]-5-methyl-1,3,4-oxadiazole (PubChem ID: 6494266), having binding energies stronger than −6 kcal/mol were found to have two common hydrogen bond interactions with VISTA active site residues: Arg54 and Arg127. The dynamics of the compound–VISTA complexes were further explored to infer binding stability of the systems. Results revealed that the compound 14187087 and 6494266 systems are highly stable with an average RMSD of 1.31 Å. Further affirmation on the results was achieved by running MM-GBSA on the MD simulation trajectories, which re-ranked 14187087 as the top-binder with a net binding energy value of −33.33 kcal/mol. In conclusion, the present study successfully predicted natural compounds that have the potential to block the function of VISTA and therefore can be utilized further in experimental studies to validate their real anti-VISTA activity.

Alzheimer's disease (AD) is prevalent among the aged and new drugs for AD are always in necessary. In this study, network pharmacology and molecular docking were adopted to bioactivity screening as well as pharmacological mechanism investigation, of phthalazinones. Those phthalazinones with certain structures and physical properties were screened out by PubChem database. And molecular docking was used to explore the potential activity of these phthalazinones as drugs for AD treatment. Secondly, network pharmacology analysis of phthalazinones and the drugs approved for AD were employed, including targets prediction, gene enrichment analysis and network analysis. Five compounds (PubChem CID: 137700766, 69619707,137700767, 137700768, 9827600) were recognized and enjoyed lower binding energy. There were 57 targets recognized as common targets that adopted to network construction. Totally 15 approved drugs with clear indications for AD were figured out, with 57 associated targets. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that phthalazinone derivatives and drugs for AD shared the same essential pathway (neuroactive ligand-receptor interaction) with associated targets. In silico analysis suggested that these phthalazinones are potential to be effective for AD treatment by regulating neuroactive ligand-receptor interaction pathway, calcium signaling pathway, and other pathways.

Over 30-35% of patients down with AML are caused by mutations of FLT3-ITD and FLT3-TKD which keeps the protein activated while it activates other signaling proteins downstream that are involved in cell proliferation, differentiation, and survival. As drug targets, many inhibitors are already in clinical practice. Unfortunately, the average overall survival rate for patients on medication suffering from AML is 5 years despite the huge efforts in this field. To perform docking simulation and ADMET studies on selected phytochemicals against FLT3 protein receptor for drug discovery against FLT3 induced AML, molecular docking simulation was performed using human FLT3 protein target (PDB ID: 6JQR) and 313 phytochemicals with standard anticancer drugs (Sorafenib and Gilteritinib in addition to other anticancer drugs). The crystal structure of the protein was downloaded from the protein data bank and prepared using Biovia Discovery Studio. The chemical structures of the phytochemicals were downloaded from the NCBI PubChem database and prepared using Open Babel and VConf softwares. Molecular docking was performed using PyRx on Autodock Vina. The ADMET properties of the best performing compounds were calculated using SwissADME and pkCMS web servers. The results obtained showed that glabridin, ellipticine and derivatives (elliptinium and 9-methoxyellipticine), mezerein, ursolic acid, formononetin, cycloartocarpesin, hypericin, silymarin, and indirubin are the best performing compounds better than sorafenib and gilteritinib based on their binding affinities. The top-performing compounds which had better binding and ADMET properties than sorafenib and gilteritinib could serve as scaffolds or leads for new drug discovery against FLT3 induced AML.Communicated by Ramaswamy H. Sarma.

Visualizing three-dimensional molecular structures is crucial to understanding and predicting their chemical behavior. Existing visualization software, however, can be cumbersome to use, and, for many, hand-drawn skeletal structures remain the preferred method of chemical communication. Although convenient, the static, two-dimensional nature of these drawings can be misleading in conveying the molecule’s 3D structure, not to mention that dynamic movement is completely disregarded. Here, we combine machine learning and augmented reality (AR) to develop MolAR, an immersive mobile application for visualizing molecules in real-world scenes. The application uses deep learning to recognize hand-drawn hydrocarbons structures which it converts into interactive 3D molecules in AR. Users can also “hunt” for chemicals in food and drink to uncover molecules in their real-life environment. A variety of interesting molecules are pre-loaded into the application, and users can visualize molecules in PubChem by providing their name or SMILES string and proteins in the Protein Data Bank by providing their PDB ID. MolAR was designed to be used in both research and education settings, providing an almost barrierless platform to visualize and interact with 3D molecular structures in a uniquely immersive way.

PurposeThis study aimed to investigate the effect of essential ingredients of licorice on the chronic obstructive pulmonary disease (COPD).MethodThe ingredients information were obtained fromPubChem(https://pubchem.ncbi.nlm.nih.gov/), related genes about COPD was collected fromgeneCards(http://www.genecards.org/).Networkpharmacology was utilized in this study.ResultThe intersection data set contains 20 molecular targets between COPD and liquorice. Protein-protein interaction network showed that there are a total of 58 nodes and 137 edges involved. The link number of AKT1 in PPI network was 39, which is the highest level of interaction. MAPK1 is an important target of Licorice on COPD.ConclusionMAPK signaling pathway could be the important key target of main ingredients of licorice on COPD.

Essential oils (EOs) from citrus fruits are excellent aromatic resources that are used in food, cosmetics, perfume, and cleaning products. EOs extracted from four citrus varieties, sweet orange, grapefruit, mandarin, and lemon, were separated into two fractions by molecular distillation. The composition, physicochemical properties, cleaning ability, and antimicrobial activity of each EO were then systematically evaluated. The relationships between each of the aforementioned characteristics are also discussed. In keeping with the principle of “like dissolves like,” most citrus EOs show better cleaning ability than acetone and all tend to dissolve the fat-soluble pigment. The key components of citrus EOs are 1-Decanol, α-terpineol, geraniol, and linalool for the inhibition of Staphylococcus aureus, Escherichia coli, Candida albicans, and Vibrio parahaemolyticus, respectively. The findings of this study will be of significant importance for the effective utilization of citrus peel resources and in the development of future applications for citrus EOs.Chemical Compounds Studied in This Article: (+)-α-Pinene (PubChem CID: 6654); β-Phellandrene (PubChem CID: 11142); 3-Carene (PubChem CID: 26049); β-Myrcene (PubChem CID: 31253); D-Limonene (PubChem CID: 440917); γ-Terpinene (PubChem CID: 7461); Octanal (PubChem CID: 454); Decanal (PubChem CID: 8175); Linalool (PubChem CID: 6549); 1-Octanol (PubChem CID: 957); β-Citral (PubChem CID: 643779); α-Terpineol (PubChem CID: 17100); Hedycaryol (PubChem CID: 5365392); α-Citral (PubChem CID: 638011); 1-Decanol (PubChem CID: 8174); Geraniol (PubChem CID: 637566).

Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure–property design rules. More than 100 million unique chemical compounds are documented in the PubChem database, and a significant sub-set of these are π-conjugated, light-absorbing molecules that might in principle act as photocatalysts. Nature has used natural selection to evolve photosynthetic assemblies; by contrast, our ability to navigate the enormous potential search space of organic photocatalysts in the laboratory is limited. Here, we integrate experiment, computation, and machine learning to address this challenge. A library of 572 aromatic organic molecules was assembled with diverse compositions and structures, selected on the basis of availability in our laboratory, rather than more sophisticated criteria. This training library was then assessed experimentally for sacrificial photocatalytic hydrogen evolution using a high-throughput, automated method. Quantum chemical calculations and machine learning were used to visualise, interpret, and ultimately to predict the photocatalytic activities of these molecules, covering a much broader chemical space than for previous polymer photocatalyst libraries. By applying unsupervised learning to the molecular structures, we identified structural features that were common in molecules with high catalytic activity. Further analysis using calculated molecular descriptors within a suite of supervised classification algorithms revealed that light absorption, exciton electron affinity, electron affinity, exciton binding energy, and singlet–triplet energy gap had correlations with the photocatalytic performance. These trained predictive models can be used in future studies as filters to deprioritise or discard would-be low-activity candidate molecules from experiments, and to prioritize more favourable candidates. As a demonstration, we used virtual in silico experiments to show that it was possible to halve the experimental cost of finding 50% of the most active photocatalysts by using the machine learning model as an experimental advisor. We further showed that the ML advisor trained on the 572-molecule library could be used to make predictions for an unseen set of 96 molecules, achieving equivalent predictive accuracies to those in the initial training set. This marks a step toward the machine-learning assisted discovery of molecular organic photocatalysts and the approach might also be applied to problems beyond photocatalytic hydrogen evolution, such as CO2 reduction and photoredox chemistry., We developed models to predict the photoactivity of organic molecules for photocatalytic hydrogen evolution by integrating experiment, computation, and machine learning. This marks a step toward the data-driven discovery of molecular photocatalysts.

Adverse Drug Reaction (ADR) prediction is one of the important tasks in drug discovery. It helps in enhancing drug safety and reducing drug discovery costs and time. Most of the existing works have focused on ADR prediction using chemical and biological properties of drugs. However, the capability of drug functions in ADR prediction has not been explored yet. ADR prediction is a multi-label classification problem and it faces the issue of class imbalance. In the present work, a methodology has been proposed for predicting ADR from drug functions. It employs the binary relevance method along with five base classifiers namely DT, ETC, KNN, MLPNN, and RF for performing multi-label classification and MLSMOTE for addressing the issue of class imbalance. The data of drug functions and ADR has been extracted respectively from SIDER and PubChem databases and then drug functions are mapped to ADR based on drug ID. After mapping drug function with the ADR, the resulted dataset comprises 670 drugs described by their functions and 6123 ADR. The proposed methodology has been applied on this dataset. The performance of the proposed methodology has been found promising in terms of accuracy, hamming loss, precision, recall, f1 score and ROC-AUC.

Signaling bias is a feature of many G protein-coupled receptor (GPCR) targeting drugs with potential clinical implications. Whether it is therapeutically advantageous for a drug to be G protein biased or β-arrestin biased depends on the context of the signaling pathway. Here, we explored GPCR ligands that exhibit biased signaling to gain insights into scaffolds and pharmacophores that lead to bias. More specifically, we considered BiasDB, a database containing information about GPCR biased ligands, and focused our analysis on ligands which show either a G protein or β-arrestin bias. Five different machine learning models were trained on these ligands using 15 different sets of features. Molecular fragments which were important for training the models were analyzed. Two of these fragments (number of secondary amines and number of aromatic amines) were more prevalent in β-arrestin biased ligands. After training a random forest model on HierS scaffolds, we found five scaffolds, which demonstrated G protein or β-arrestin bias. We also conducted t-SNE clustering, observing correspondence between unsupervised and supervised machine learning methods. To increase the applicability of our work, we developed a web implementation of our models, which can predict bias based on user-provided SMILES, drug names, or PubChem CID. Our web implementation is available at: drugdiscovery.utep.edu/biasnet.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database that serves scientific communities as well as the general public. This database collects chemical information from hundreds of data sources and organizes them into multiple data collections, including Substance, Compound, BioAssay, Protein, Gene, Pathway, and Patent. These collections are interlinked with each other, allowing users to discover related records in the various collections (e.g., drugs targeting a protein or genes modulated by a chemical). PubChem can be searched by keyword (e.g., a chemical, protein, or gene name) as well as by chemical structure. The input structure can be provided using popular line notations or drawn with the PubChem Sketcher. PubChem supports various types of structure searches, including identity search, 2‐D and 3‐D similarity searches, and substructure and superstructure searches. Results from multiple searches can be combined using Boolean operators (i.e., AND, OR, and NOT) to formulate complex queries. PubChem allows the user to quickly retrieve a list of records annotated with a particular classification or ontological term. This paper provides step‐by‐step instructions on how to explore PubChem data with examples of commonly requested tasks. © 2021. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Finding genes and proteins that interact with a given compound Basic Protocol 2: Finding drug‐like compounds similar to a query compound through a two‐dimensional (2‐D) similarity search Basic Protocol 3: Finding compounds similar to a query compound through a three‐dimensional (3‐D) similarity search Support Protocol: Computing similarity scores between compounds Basic Protocol 4: Getting the bioactivity data for the hit compounds from substructure search Basic Protocol 5: Finding drugs that target a particular gene Basic Protocol 6: Getting bioactivity data of all chemicals tested against a protein. Basic Protocol 7: Finding compounds annotated with classifications or ontological terms Basic Protocol 8: Finding stereoisomers and isotopomers of a compound through identity search