Abstract: Herbal formulas from traditional Chinese medicines (TCMs) have been extensively used in clinics as effective therapies, but it is still a great challenge to demonstrate the scientific basis for their therapeutic effects at the level of molecular biology. By taking a classic herbal formula (Yu Ping Feng decoction, YPF) as an example, this study developed a novel network pharmacology based method to identify its potential therapeutic targets. First, this study constructed a "targets-(pathways)-targets" (TPT) network in which targets of YPF were connected by relevant pathways; then, this network was decomposed into separate modules with strong internal connections; lastly, the propensity of each module toward different diseases was assessed by a contribution score. On the basis of a significant association between network modules and therapeutic diseases validated by chi-square test (p-value < 0.001), this study identified the network module with the strongest propensity toward therapeutic diseases of YPF. Further, the targets with the highest centrality in this module are recommended as YPF's potential therapeutic targets. By integrating the complicated "multi-targets-multi-pathways-multi-diseases" relationship of herbal formulas, the method shows promise for identifying its potential therapeutic targets, which could contribute to the modern scientific illustration of TCMs' traditional clinical applications.

Abstract: Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery.

Abstract: Natural medicines (i.e., herbal medicines, traditional formulas) are useful for treatment of multifactorial and chronic diseases. Here, we present KampoDB ( http://wakanmoview.inm.u-toyama.ac.jp/kampo/ ), a novel platform for the analysis of natural medicines, which provides various useful scientific resources on Japanese traditional formulas Kampo medicines, constituent herbal drugs, constituent compounds, and target proteins of these constituent compounds. Potential target proteins of these constituent compounds were predicted by docking simulations and machine learning methods based on large-scale omics data (e.g., genome, proteome, metabolome, interactome). The current version of KampoDB contains 42 Kampo medicines, 54 crude drugs, 1230 constituent compounds, 460 known target proteins, and 1369 potential target proteins, and has functional annotations for biological pathways and molecular functions. KampoDB is useful for mode-of-action analysis of natural medicines and prediction of new indications for a wide range of diseases.

Abstract: Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, the proposed method is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database. It is shown that the graph-based model outperforms SMILES based models in a variety of metrics, especially in the rate of valid outputs. For the application of drug design tasks, conditional graph generative model is employed. This method offers highe flexibility and is suitable for generation based on multiple objectives. The results have demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold, compounds with specific drug-likeness and synthetic accessibility requirements, as well as dual inhibitors against JNK3 and GSK-3beta.

Abstract: Biosensor technology has great potential for the detection of cancer through tumor-associated molecular biomarkers. In this work, we describe the immobilization of the recombinant humanized anti-HER2 monoclonal antibody (trastuzumab) on a silver nanostructured plate made by pulsed laser deposition (PLD), over a thin film of Au(111). Immobilization was performed via 4-mercapto benzoic acid self-assembled monolayers (4-MBA SAMs) that were activated with coupling reagents. A combination of immunofluorescence images and z-stack analysis by confocal laser scanning microscopy (CLSM) allowed us to detect HER2 presence and distribution in the cell membranes. Four different HER2-expressing breast cancer cell lines (SKBR3 +++, MCF-7 +/-, T47D +/-, MDA-MB-231 -) were incubated during 24 h on functionalized silver nanostructured plates (FSNP) and also on Au(111) thin films. The cells were fixed by means of an ethanol dehydration train, then characterized by atomic force microscopy (AFM) and surface-enhanced Raman scattering (SERS). SERS results showed the same tendency as CLSM findings (SKBR3 > MCF-7 > T47D > MDA-MB-231), especially when the Raman peak associated with phenylalanine amino acid (1002 cm(-1)) was monitored. Given the high selectivity and high sensitivity of SERS with a functionalized silver nanostructured plate (FSNP), we propose this method for identifying the presence of HER2 and consequently, of breast cancer cells.

Abstract: Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

Abstract: About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.

Abstract: BACKGROUND: Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download. RESULTS: We compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis. CONCLUSION: The rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion.

Abstract: Here we identify hundreds of new drug-disease associations for over 900 FDA-approved drugs by quantifying the network proximity of disease genes and drug targets in the human (protein-protein) interactome. We select four network-predicted associations to test their causal relationship using large healthcare databases with over 220 million patients and state-of-the-art pharmacoepidemiologic analyses. Using propensity score matching, two of four network-based predictions are validated in patient-level data: carbamazepine is associated with an increased risk of coronary artery disease (CAD) [hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.12-2.18], and hydroxychloroquine is associated with a decreased risk of CAD (HR 0.76, 95% CI 0.59-0.97). In vitro experiments show that hydroxychloroquine attenuates pro-inflammatory cytokine-mediated activation in human aortic endothelial cells, supporting mechanistically its potential beneficial effect in CAD. In summary, we demonstrate that a unique integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data complemented by mechanistic in vitro studies can facilitate drug repurposing.

Abstract: Micro-pollutants such as 17beta-Estradiol (E2) have been detected in different water resources and their negative effects on the environment and organisms have been observed. Aptamers are established as a possible detection tool, but the underlying ligand binding is largely unexplored. In this study, a previously described 35-mer E2-specific aptamer was used to analyse the binding characteristics between E2 and the aptamer with a MD simulation in an aqueous medium. Because there is no 3D structure information available for this aptamer, it was modeled using coarse-grained modeling method. The E2 ligand was positioned inside a potential binding area of the predicted aptamer structure, the complex was used for an 25 ns MD simulation, and the interactions were examined for each time step. We identified E2-specific bases within the interior loop of the aptamer and also demonstrated the influence of frequently underestimated water-mediated hydrogen bonds. The study contributes to the understanding of the behavior of ligands binding with aptamer structure in an aqueous solution. The developed workflow allows generating and examining further appealing ligand-aptamer complexes.