99 research outputs found
Computational frameworks to aid pharmacological studies : Tools, Databases and Prediction models
No full textRational approaches to the traditional drug discovery process rely on high-throughput and lowthroughput bioactivity or phenotypic screening studies. Such profiling strategies have been the foremost step utilized when identifying or curating lead molecules, and more recently, when evaluating compound repositioning/repurposing opportunities. Although promising, such systematic approaches are inherently limited by their cost and labor constraints, and the necessity of specific screening equipment’s renders them outside the scope of many academic laboratories. Further, elucidation of any novel associations through such experimental techniques require exhaustive searches of compound libraries. Hence, many positive drug indications revealed, though rational, are often by the virtue of serendipity.
The accumulation and standardization of existing compound profiling datasets has paved the way to the field of chemoinformatics. Wherein, data-driven computational approaches are employed as a pragmatic solution to challenge the inherent notion of serendipity in screening studies. Such in silico models serve as an efficient and cost-effective augmentation to the experimental screening approaches, circumventing the intrinsic limitations of current drug discovery methods. This study therefore was motivated towards identifying niches and limitations prevalent in the current pharmacological paradigm, where an effective computational framework could be utilized to complement and expedite the conventional drug discovery process. The various computational approaches proposed in this article-based thesis include exploratory web-tools, new data resources, and prediction models, that are introduced as supplements to extend the current computer-aided drug discovery process (CADD).
Firstly, I developed a web-application, termed C-SPADE, a novel compound-centric chemoinformatic tool that facilitates interactive analysis and visualization of compound screening experiments using Compound-SPecific bioActivity DEndrograms. The tool employs compound-compound similarity metrics to estimate the diversity amidst compounds profiled in the screening panel and intuitively represents the chemical similarity space and the observed bioactivity values. The web-tool provides users an exploratory framework to perform pharmacological analysis and investigate novel compound associations employing diverse compound similarity clusters. Secondly, to address the heterogeneous and non-standardized bioactivity data in existing data resources, a comprehensive open-data platform, called Drug Target Commons (DTC), was developed. DTC feature tools for data annotation, standardization, curation to address intra-resource heterogeneity and provide users a one-stop resource for drug discovery and in silico model development endeavours. User-specific applications of both the above-mentioned resources have been demonstrated through several case studies and experimental validations.
In addition to the tools and databases, I have also designed and implemented diverse machine learning models primarily to predict potent compound-kinase interactions and to fill the current experimental gaps in large-scale activity profiling studies. Firstly, through collaborative efforts, we employed the Kronecker kernel-based regularized least square regression (KronRLS) algorithm under different crossvalidation settings to predict both the uncharacterised binding measures in large-scale profiling studies and novel compound-target associations. As a case study, the off-target profile of an investigational VEGF receptor inhibitor tivozanib was predicted and experimentally validated. Secondly, I designed and implemented an efficient statistical model utilizing an ensemble SVM classifier to prioritize potent compound-kinase association for biochemical testing. The developed computational framework was termed Virtual Kinome Profiler (VKP) and was efficiently used in compound repositioning and lead identification studies, wherein 19 novel kinase-compound interactions spanning across different kinases were predicted and experimentally validated. Apart from elucidating the chemogenomic similarities prevalent among distinct kinase proteins, VKP with a positive prediction value (PPV) of 84% was shown to reduce the time and cost constraints related to traditional experimental screening process.
Most of the computational frameworks proposed in this thesis are designed and deployed with an accompanying web-based graphic user interface (GUI). This in turn aids in the translatability of the platforms, overcoming the current prerequisites required when utilizing CADD models, including prior expertise in data analysis and scripting languages. These studies together exemplify new applications of computational models in diverse areas of the drug discovery process, subsequently making invaluable augmentations to a chemical biologist’s toolbox.ei saavutettav
Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery
No full textIntroduction: Polypharmacology has emerged as an essential paradigm for modern drug discovery process. Multiple lines of evidence suggest that agents capable of modulating multiple targets in a selective manner may offer also improved balance between therapeutic efficacy and safety compared to single-targeted agents. Areas covered: Herein, the authors review the recent progress made in experimental and computational strategies for addressing the critical challenges with rational discovery of selective multi-targeted agents within the context of polypharmacological modelling. Specific focus is placed on multi-targeted mono-therapies, although examples of combinatorial polytherapies are also covered as an important part of the polypharmacology paradigm. The authors focus mainly on anti-cancer treatment applications, where polypharmacology is playing a key role in determining the efficacy-toxicity trade-off of multi-targeting strategies. Expert opinion: Even though it is widely appreciated that complex polypharmacological interactions can contribute both to therapeutic and adverse side-effects, systematic approaches for improving this balance by means of integrated experimental-computational strategies are still lacking. Future developments will be needed for comprehensive collection and harmonization of systems-wide target selectivity data, enabling better utilization and control for multi-targeted activities in the drug development process. Additional areas of future developments include model-based strategies for drug combination screening and improved pre-clinical validation options with animal models.Peer reviewe
C-SPADE : a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms
No full textThe advent of polypharmacology paradigm in drug discovery calls for novel chemoinformatic tools for analyzing compounds' multi-targeting activities. Such tools should provide an intuitive representation of the chemical space through capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects. Most of the existing compound-centric chemoinformatics tools lack interactive options and user interfaces that are critical for the real-time needs of chemical biologists carrying out compound screening experiments. Toward that end, we introduce C-SPADE, an open-source exploratory web-tool for interactive analysis and visualization of drug profiling assays (biochemical, cell-based or cell-free) using compound-centric similarity clustering. C-SPADE allows the users to visually map the chemical diversity of a screening panel, explore investigational compounds in terms of their similarity to the screening panel, perform polypharmacological analyses and guide drug-target interaction predictions. C-SPADE requires only the raw drug profiling data as input, and it automatically retrieves the structural information and constructs the compound clusters in real-time, thereby reducing the time required for manual analysis in drug development or repurposing applications. The web-tool provides a customizable visual workspace that can either be downloaded as figure or Newick tree file or shared as a hyperlink with other users. C-SPADE is freely available at http://cspade.fimm.fi/.Peer reviewe
URI Disambiguation in the Context of Linked Data
No full textThe Linked Data initiative has given rise to an increasing number of RDF datasets, many of which are freely accessible online. These resources often arise as a result of database exports; however sufficient consideration may not be given to the unseen implications caused when they are used in the wider context of the Semantic Web. This paper investigates two popular resources, DBLP and DBpedia, and discusses whether the issues regarding identity management and co-reference resolution have been suitably addressed. We find that a large percentage of authors in DBLP have been conflated, and that disambiguation pages have been incorrectly linked using owl:sameAs within DBpedia. Systems for dealing with these issues are presented, and directions are given for future research
A study on Accounting students' perception on employability skills in University of Technology and Applied Sciences, Muscat
No full textAbstract: Universities are expected to contribute the economic growth of the country by preparing the graduates with necessary academic skills. Students are focusing more on knowledge or the course contents but put less effort on the skill that are inventible for them to master in their chosen field. This study is focusing on the perception of accounting graduates of UTAS about employability skill that they should possess. The study found that the students feel Learning and Development skills are not necessary for employability irrespective of gender or the year of study. Learning and Development skills include working in teams, able to adapt technology, communication skills, lifelong learning and problem solving skills. When subject knowledge is compared with the year of study, the authors found that the students did not give importance to subject knowledge as well. However, Life/Career skills are considered to be valuable skills by the students whether it is compared with gender or year of study. The authors recommend that the students must be educated on the importance of Learning and development skills and subject knowledge, which will help them in securing a good job in the future. It will also help to solve the unemployment problems in the society.
Keywords: Employability skills, subject knowledge, students, University, Accounting, Unemployment problems.
Title: A study on Accounting students’ perception on employability skills in University of Technology and Applied Sciences, Muscat
Author: Dr. Anitha Ravikumar, Mr. Gopalan Puthukulam, Dr. Anupam Sharma
International Journal of Recent Research in Commerce Economics and Management (IJRRCEM)
ISSN 2349-7807
Vol. 9, Issue 3, July 2022 - September 2022
Page No: 145-149
Paper Publications
Website: www.paperpublications.org
Published Date: 23-September-2022
DOI: https://doi.org/10.5281/zenodo.7108137
Paper Download Link (Source)
https://www.paperpublications.org/upload/book/A%20study%20on%20Accounting%20students-23092022-6.pdfInternational Journal of Recent Research in Commerce Economics and Management (IJRRCEM), ISSN 2349-7807, Paper Publications, Website: www.paperpublications.or
Leveraging multiple data types for improved compound-kinase bioactivity prediction
No full textAbstract Machine learning provides efficient ways to map compound-kinase interactions. However, diverse bioactivity data types, including single-dose and multi-dose-response assay results, present challenges. Traditional models utilize only multi-dose data, overlooking information contained in single-dose measurements. Here, we propose a machine learning methodology for compound-kinase activity prediction that leverages both single-dose and dose-response data. We demonstrate that our two-stage approach yields accurate activity predictions and significantly improves model performance compared to training solely on dose-response labels. This superior performance is consistent across five diverse machine learning methods. Using the best performing model, we carried out extensive experimental profiling on a total of 347 selected compound-kinase pairs, achieving a high hit rate of 40% and a negative predictive value of 78%. We show that these rates can be improved further by incorporating model uncertainty estimates into the compound selection process. By integrating multiple activity data types, we demonstrate that our approach holds promise for facilitating the development of training activity datasets in a more efficient and cost-effective way
Effect of oxidation on the structural, vibrational, magnetic and electrical properties of Fe thin films
No full textElectrochemical studies of aluminium 7075 reinforced with Al2O3/SiCp hybrid composites in acid chloride medium
No full textInfluence of radiation on MHD peristaltic blood flow through a tapered channel in presence of slip and joule heating
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