Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

BackgroundA number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows.ResultsUsing a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database.ConclusionsWe present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/

Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch

In this paper we are sketching the chemical structure of suppressor drug for autism spectrum disorder using a computational tool. Here we are designing three molecular compounds like Fluoxetine, Risperidone, Melatonin. Structuring the suppressors, sketching the aromatization and bonding of the functional groups with the elements like Oxygen, Nitrogen, halogens. In our work we are using computational algorithm for drawing the structure of suppressor drug. In this paper we are mentioning the autism spectrum suppressor’s molecular formula as well as structural formula

The influence of social media political marketing on trust, loyalty and voting intention of youth voters in South Africa

A research report submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Management in the field of Strategic Marketing Johannesburg, 2016South Africa has witnessed a decline in youth voter turnout. Consequently, political parties are integrating social media in their political marketing strategies in order to appeal to the youth voters. Notwithstanding the cumulative research that has been conducted on social media political marketing globally, there is dearth of such research in South Africa. Furthermore, no studies have explored the influence of social media political marketing on voter trust, loyalty and voting intention of the youth in the South African political context. This research intends to contribute to the increasing knowledge on the efficacy of social media political marketing by political parties in South Africa to engage with the youth and improve their election turn out. The two main research objectives the study seeks to achieve are to establish the influence of social media political marketing on voting intention, with voter trust and voter loyalty as mediators and to determine which mediator (voter trust or voter loyalty) has the strongest influence on the outcome variable (voting intention). Using a data set of 250 respondents, between the ages of 18 and 35 years, from Gauteng Province in South Africa, this study explores these relationships. The study outcome is that all five hypotheses are supported. The results denote that the relationship between social media political marketing and voter trust, social media political marketing and voter loyalty, voter trust and voter loyalty, voter trust and voting intention and voter loyalty and voting intention are all positive in a significant way. The research paper deliberates on the implications of the results from an academic, political party, legal and marketers’ perspective. In addition, directions for future research are suggested.MT201

Machine-learning approaches in drug discovery: methods and applications

During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and representative training-set compounds to learn robust decision rules. The explosive growth in the amount of public domain-available chemical and biological data has generated huge effort to design, analyze, and apply novel learning methodologies. Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances

Washington University Senior Honor Thesis Abstracts (WUSHTA), Spring 2017

Complete issue of the Washington University Senior Honors Thesis Abstracts (WUSHTA), Spring 2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich Editor; Kristin G. Sobotka, Programs Manager; Jennifer Kohl

The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4), and features an unprecedented combination of nearest neighbor (NN) searches and Naïve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at ppb2.gdb.tools

African Politics in the Digital Age: A Study of Political Party-Social Media Campaign Strategies in Ghana

Digital media is transforming politics. It has made it imperative for political stakeholders to come up with new strategies that respond to challenges triggered by the new digital communication platforms. Equally, the technological developments have affected communication processes and strategies in transitional political contexts, with varying impacts on democratic governance, political participation and forms of deliberation for citizens. However, the actual impact of social media on political processes remains debatable. Many issues emerge including not only how communications technologies revitalise campaign techniques but also how they influence actors, organisations and reorient political campaigning environments. In Africa, it is important to ask in specific contexts how the new technologies are reconfiguring the relationship between the rulers and the ruled, between politicians and the electorate. In particular, how has digital media facilitated new forms of political communications to individuals and groups? Has it gone beyond geography, class, gender, language or race? What has been the specific impact on campaign strategies, and their process and impact on electoral politics in countries such as Ghana, an emerging democracy? Through a case study, this research has explored the changing dynamics of election campaigning in Ghana in the context of social media. By examining the influence of Facebook, Twitter Instagram and other Social Network Sites (SNSs) for political campaigning, the research produces an original analysis of digital political communication, organization and mobilization, among others, as they are deployed by the main political parties, namely, the National Democratic Congress (NDC) and the New Patriotic Party (NPP), with a focus on the 2012 and 2016 elections. The study has adopted a qualitative research methodology, based on in-depth interviews (formal and informal), focus group discussions, as well as informal observation techniques, which were applied to gather original evidence. The main findings are that social media is implicated in political campaigns in multiple ways, with its ability to change, and are dependent on the availability of resources and policy frameworks that regulate and streamline their usages. The study shows how the campaign process is also implicated by political organizations, actors and voters, rather than just by the technologies. The research has uncovered the role of offline/digital ‘serial callers’, those quasi political communicators hired by political parties to influence political campaigning. Challenges and limitations notwithstanding, the research provides an invaluable insight into the relationship between the use of social media for political communication and its ramifications for democratization in Ghana. It contributes original insights on the shifts and impact of political communication within the African context