PubChem structure-activity relationship (SAR) clusters

Rationally Minimizing Natural Product Libraries Using Mass Spectrometry

Natural product libraries are crucial to drug development, but large libraries drastically increase the time and cost during initial high throughput screens. Here, we developed a method that leverages liquid chromatography-tandem mass spectrometry spectral similarity to dramatically reduce library size, with minimal bioactive loss. This method offers a broadly applicable strategy for accelerated drug discovery with cost reductions, which enable implementation in resource-limited settings.

Examining unsupervised ensemble learning using spectroscopy data of organic compounds

One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and stable solutions across different domains and datasets. Unfortunately, not all clusterings in the ensemble contribute to the final data partition. Cluster ensemble selection (CES) aims at selecting a subset from a large library of clustering solutions to form a smaller cluster ensemble that performs as well as or better than the set of all available clustering solutions. In this paper, we investigate four CES methods for the categorization of structurally distinct organic compounds using high-dimensional IR and Raman spectroscopy data. Single quality selection (SQI) forms a subset of the ensemble by selecting the highest quality ensemble members. The Single Quality Selection (SQI) method is used with various quality indices to select subsets by including the highest quality ensemble members. The Bagging method, usually applied in supervised learning, ranks ensemble members by calculating the normalized mutual information (NMI) between ensemble members and consensus solutions generated from a randomly sampled subset of the full ensemble. The hierarchical cluster and select method (HCAS-SQI) uses the diversity matrix of ensemble members to select a diverse set of ensemble members with the highest quality. Furthermore, a combining strategy can be used to combine subsets selected using multiple quality indices (HCAS-MQI) for the refinement of clustering solutions in the ensemble. The IR + Raman hybrid ensemble library is created by merging two complementary "views" of the organic compounds. This inherently more diverse library gives the best full ensemble consensus results. Overall, the Bagging method is recommended because it provides the most robust results that are better than or comparable to the full ensemble consensus solutions.

Discovery of the signal pathways and major bioactive compounds responsible for the anti-hypoxia effect of Chinese cordyceps

ETHNOPHARMACOLOGICAL RELEVANCE

Hypoxia will cause an increase in the rate of fatigue and aging. Chinese cordyceps, a parasitic Thitarodes insect-Ophiocordyceps sinensis fungus complex in the Qinghai-Tibet Plateau, has long been used to ameliorate human conditions associated with aging and senescence, it is principally applied to treat fatigue, night sweating and other symptoms related to aging, and it may play the anti-aging and anti-fatigue effect by improving the body's hypoxia tolerance.

AIMS OF THE STUDY

The present study investigated the anti-hypoxia activity of Chinese cordyceps and explore the main corresponding signal pathways and bioactive compounds.

MATERIALS AND METHODS

In this study, network pharmacology analysis, molecular docking, cell and whole pharmacodynamic experiments were hired to study the major signal pathways and the bioactive compounds of Chinese cordyceps for anti-hypoxia activity.

RESULTS

17 pathways which Chinese cordyceps acted on seemed to be related to the anti-hypoxia effect, and "VEGF signal pathway" was one of the most important pathway. Chinese cordyceps improved the survival rate and regulated the targets related VEGF signal pathway of H9C2 cells under hypoxia, and also had significant anti-hypoxia effects to mice. Chorioallantoic membrane model experiment showed that Chinese cordyceps and the main constituents of (9Z,12Z)-octadeca-9,12-dienoic acid and cerevisterol had significant angiogenic activity in hypoxia condition.

CONCLUSION

Based on the results of network pharmacology and molecular docking analysis, cell and whole pharmacodynamic experiments, promoting angiogenesis by regulating VEGF signal pathway might be one of the mechanisms of anti-hypoxia effect of Chinese cordyceps, (9Z, 12Z)-octadeca-9,12-dienoic acid and cerevisterol were considered as the major anti-hypoxia bioactive compounds in Chinese cordyceps.

A network pharmacology approach to investigate the blood enriching mechanism of Danggui buxue Decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Danggui buxue Decoction (DBD) has been frequently used to treat with blood deficiency, which consisted of Danggui (DG) and Huangqi (HQ) at a ratio of 1:5. Accumulating evidence showed that blood deficiency in traditional Chinese medicine (TCM) was similar to anemia in modern medicine.

AIM OF THE STUDY

The purpose of this study was to explore its therapeutic mechanism of with network pharmacology approach.

MATERIALS AND METHODS

We explored the chemical compounds of DBD and used compound ADME screening to identify the potential compounds. Targets for the therapeutic actions of DBD were obtained from the PharmMapper, Swiss, SEA and STITCH. GO analysis and pathway enrichment analysis was performed using the DAVID webserver. Cytoscape was used to visualize the compound-target-pathway network for DBD. The pharmacodynamics and crucial targets were also validated.

RESULTS

Thirty-six potential active components in DBD and 49 targets which the active components acted on were identified. 47 KEGG pathways which DBD acted on were also come to light. And then, according to KEGG pathway annotation analysis, only 16 pathways seemed to be related to the blood nourishing effect of DBD, such as PI3K-AKT pathway, and so on. Only 32 targets participated in these 16 pathways and they were acted on by 29 of the 36 active compounds. Whole pharmacodynamic experiments showed that DBD had significant effects to blood loss rats. Furthermore, DBD could promote the up-regulation of hematopoietic and immune related targets and the down-regulation of inflammatory related targets. Significantly, with the results of effective rate, molecular docking and experimental validation, we predicted astragaloside IV in HQ, senkyunolide A and senkyunolide K in DG might be the major contributing compounds to DBD's blood enriching effect.

CONCLUSION

In this study, a systematical network pharmacology approach was built. Our results provided a basis for the future study of senkyunolide A and senkyunolide K as the blood enriching compounds in DBD. Furthermore, combined network pharmacology with validation experimental results, the nourishing blood effect of DBD might be manifested by the dual mechanism of enhancing immunity and promoting hematopoiesis.

Accessing Expert-Curated Pharmacological Data in the IUPHAR/BPS Guide to PHARMACOLOGY

The IUPHAR/BPS Guide to PHARMACOLOGY is an expert-curated, open-access database of information on drug targets and the substances that act on them. This unit describes the procedures for searching and downloading ligand-target binding data and for finding detailed annotations and the most relevant literature. The database includes concise overviews of the properties of 1,700 data-supported human drug targets and related proteins, divided into families, and 9,000 small molecule and peptide experimental ligands and approved drugs that bind to those targets. More detailed descriptions of pharmacology, function, and pathophysiology are provided for a subset of important targets. The information is reviewed regularly by expert subcommittees of the IUPHAR Committee on Receptor Nomenclature and Drug Classification. A new immunopharmacology portal has recently been added, drawing together data on immunological targets, ligands, cell types, processes and diseases. The data are available for download and can be accessed computationally via Web services. © 2018 by John Wiley & Sons, Inc.

DrPOCS: Drug Repositioning Based on Projection Onto Convex Sets

Drug repositioning, i.e., identifying new indications for known drugs, has attracted a lot of attentions recently and is becoming an effective strategy in drug development. In literature, several computational approaches have been proposed to identify potential indications of old drugs based on various types of data sources. In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS). With the integration of drug structure and disease phenotype information, DrPOCS predicts potential associations between drugs and diseases with matrix completion. Benchmarking results demonstrate that our proposed approach outperforms popular existing approaches with high accuracy. In addition, a number of novel predicted indications are validated with various types of evidences, indicating the predictive power of our proposed approach.

In silico insights into the identification of potential novel angiogenic inhibitors against human VEGFR-2: a new SAR-based hierarchical clustering approach

In this study, binding efficiency of new pyrrolopyrimidine structural analogs against human vascular endothelial growth factor receptor-2 (VEGFR-2) were elucidated using integrated in silico methods. Optimized high-resolution model of VEGFR-2 was generated and adopted for structure-based virtual screening approaches. Pyrrolopyrimidine inhibitor (CP15) associated compounds were screened from PubChem database and subjected to virtual screening and comparative docking methods against the receptor ligand-binding domain. Accordingly, high efficient compounds were clustered with similarity indices through PubChem structure cluster module using single-linkage algorithm. Moreover, pharmacokinetics including drug metabolism activities of high-binding leads under investigation was portrayed using ADMET and similarity ensemble analysis. Optimal energy orientations of the selected protein model have been shown to be reliable, and highly recommended for screening and docking studies. Docking and clustering strategies were shown that nineteen candidates as most effective binders for VEGFR-2 than CP15, and are grouped as three classes. Lys⁸⁶⁸, Glu⁸⁸⁵, Cys⁹¹⁹, His¹⁰²⁶, Arg¹⁰²⁷, Asp¹⁰⁴⁶, and Gly¹⁰⁴⁸ residues were predicted as novel hotspot residues, and participate in H-bonds, π-cation, π-stacking, halogen bonds, and salt-bridges formation with ligands. These additional bonds are contributing extent stability that holds the receptor structure at flexible state, this make difficult to any further conformational changes for evoking angiogenic signals. The ADMET and similarity ensemble analysis results were strongly indicated that thirteen candidates as best ligands for angiogenesis targets. Altogether, these findings indicate potential angiogenic templates and their binding levels with VEGFR-2; sorted viewpoints could be useful as a promising way to describe potential angiogenesis inhibitors with related molecular targets.

In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach

In the present study, we have focused on to elucidate potential bioactive pyrrolopyridine (PYP23) analogs against human mitogen-activated protein kinase-activated protein kinase-2 (MK-2). Here, in silico methods and computational systems biology tools were used as rational strategies to predict novel PYP23 analogs against the MK-2. Initially, crystal structure (PDB-ID: 2P3G) consists steriochemical conflicts were rectified by structure-optimization approaches using the Modeller program, and a new optimized-high resolution model was generated. The stereochemical qualities of the predicted MK-2 model were judged; these showed that the model was reliable for docking assessments. SAR-based bioactivity analysis showed that among the 197 datasets only 15 candidates contained bioactivity data and were accepted as probable MK-2 inhibitors. Virtual screening and docking strategies of dataset compounds against the ligand-binding domain of MK-2 recognized 13 composites containing high binding affinity than known compounds. Furthermore, the comparative structure clustering, in silico toxicogenomics and QSAR-based anticancer properties prediction approaches were successful in the recognition of five best potential compounds such as 60118340, 60118338, 60117736, 60118473 and 60118322, which have great anticancer and drug-likeness with non-toxicity class indices. Leu⁷⁰, Glu¹³⁹, Leu¹⁴¹, Glu¹⁴⁵, Glu¹⁹⁰, Thr²⁰⁶ and Asp²⁰⁷ were found to be novel hotspot residues prominently involved in H-bonds framing with ligands. Interestingly, they have shown better molecular similarity with known bioactive PYP inhibitors. Thus, predicted five compounds can useful as possible chemotherapeutic agents for MK-2 and show similar molecular actions like known PYP inhibitors. Overall, these streamlined new methods may have great potential to reveal possible ligands toward other molecular targets and biomarkers.

Computational screening, ensemble docking and pharmacophore analysis of potential gefitinib analogues against epidermal growth factor receptor

The observable mutated isoforms of epidermal growth factor receptor (EGFR) are important considerable therapeutic benchmarks in moderating the non-small cell lung cancer (NSCLC). Recently, quinazoline-based ATP competitive inhibitors have been developed against the EGFR; however, these imply the mutation probabilities, which contribute to the discovery of high probable novel inhibitors for EGFR mutants. Therefore, SAR-based bioactivity analysis, molecular docking and computational toxicogenomics approaches were performed to identify and evaluate new analogs of gefitinib against the ligand-binding domain of the EGFR double-mutated model. From the diverse groups of molecular clustering and molecular screening strategies, top high-binding gefitinib-analogues were identified and studied against EGFR core cavity through three-phase ensemble docking approach. Resulted high possible leads showed good binding orientations than gefitinib (positive control) thus they were subjected to pharmacophore analysis that possesses possible molecular assets to tight binding with EGFR domain. Residues Ser720, Arg841 and Trp880 were observed as novel hot spots and involved in H-bonds, pi-stacking and π-cation interactions that contribute additional electrostatic potency to sustain stability and complexity of protein-ligand complexes, thus they have ability to profoundly adopted by pharmacophoric features. Furthermore, lead molecules have an inhibition percent probability, anticancer potency, toxic impacts, flexible pharmacokinetics, potential gene-chemical interactions towards EGFR were revealed by computational systems biology tools. Our multiple screening strategies confirmed that the druggable sub-pocket was crucial to strong EGFR-ligand binding. The essential pharmacophoric features of ligands provided viewpoints for new inhibitors envisaging, and predicted scaffolds could used as anticancer agents against selected EGFR mutated isoforms.

Finding Potential Multitarget Ligands Using PubChem

PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a key chemical information resource, developed and maintained by the US National Institutes of Health. The present chapter describes how to find potential multitarget ligands from PubChem that would be tested in further experiments. While the protocol presented here uses PubChem's Web-based interfaces to allow users to follow it interactively, it can also be implemented in computer software by using programmatic access interfaces to PubChem (such as PUG-REST or E-Utilities).

Data Sharing in the Pharmaceutical Enterprise: The Genie's Out of the Bottle

OBJECTIVE

This Commentary shows that the present emphasis on the sharing of data from clinical trials can be extended to the entire pharmaceutical enterprise.

METHODS

The authors constructed a Data Sharing Dashboard that shows the relationship between all of the life-cycle domains of the pharmaceutical enterprise from discovery to obsolescence and the domain-bridging disciplines, such as target credentialing, structure-activity relationships, and exposure-effect relationships.

FINDINGS

The published literature encompassing the pharmaceutical enterprise is expansive, covering the major domains of discovery, translation, clinical development, and post-marketing outcomes research, all of which have even larger, though generally inaccessible, troves of legacy data bases. Notable exceptions include the fields of genomics and bioinformatics.

IMPLICATIONS

We have the opportunity to broaden the present momentum of interest in data sharing to the entire pharmaceutical enterprise, beginning with discovery and extending into health technology assessment and post-patent expiry generic use with the plan of integrating new levels and disciplines of knowledge and with the ultimate goal of improving the care of our patients.

How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity

Background

Hierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This situation is usually disregarded and conclusions are based on a single result, leading to questions concerning the permanence of clusters in all the resulting dendrograms; this happens, for example, when using HCA for grouping molecular descriptors to select that less similar ones in QSAR studies.

Results

Representing dendrograms in graph theoretical terms allowed us to introduce four measures of cluster frequency in a canonical way, and use them to calculate cluster frequencies over the set of all possible dendrograms, taking all ties in proximity into account. A toy example of well separated clusters was used, as well as a set of 1666 molecular descriptors calculated for a group of molecules having hepatotoxic activity to show how our functions may be used for studying the effect of ties in HCA analysis. Such functions were not restricted to the tie case; the possibility of using them to derive cluster stability measurements on arbitrary sets of dendrograms having the same leaves is discussed, e.g. dendrograms from variations of HCA parameters. It was found that ties occurred frequently, some yielding tens of thousands of dendrograms, even for small data sets.

Conclusions

Our approach was able to detect trends in clustering patterns by offering a simple way of measuring their frequency, which is often very low. This would imply, that inferences and models based on descriptor classifications (e.g. QSAR) are likely to be biased, thereby requiring an assessment of their reliability. Moreover, any classification of molecular descriptors is likely to be far from unique. Our results highlight the need for evaluating the effect of ties on clustering patterns before classification results can be used accurately.Graphical abstract

The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, http://www.guidetopharmacology.org) provides expert-curated molecular interactions between successful and potential drugs and their targets in the human genome. Developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS), this resource, and its earlier incarnation as IUPHAR-DB, is described in our 2014 publication. This update incorporates changes over the intervening seven database releases. The unique model of content capture is based on established and new target class subcommittees collaborating with in-house curators. Most information comes from journal articles, but we now also index kinase cross-screening panels. Targets are specified by UniProtKB IDs. Small molecules are defined by PubChem Compound Identifiers (CIDs); ligand capture also includes peptides and clinical antibodies. We have extended the capture of ligands and targets linked via published quantitative binding data (e.g. Ki, IC50 or Kd). The resulting pharmacological relationship network now defines a data-supported druggable genome encompassing 7% of human proteins. The database also provides an expanded substrate for the biennially published compendium, the Concise Guide to PHARMACOLOGY. This article covers content increase, entity analysis, revised curation strategies, new website features and expanded download options.

PubChem Substance and Compound databases

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.