CyTargetLinker app update: A flexible solution for network extension in Cytoscape

Here, we present an update of the open-source CyTargetLinker app for Cytoscape ( http://apps.cytoscape.org/apps/cytargetlinker) that introduces new automation features. CyTargetLinker provides a simple interface to extend networks with links to relevant data and/or knowledge extracted from so-called linksets. The linksets are provided on the CyTargetLinker website ( https://cytargetlinker.github.io/) or can be custom-made for specific use cases. The new automation feature enables users to programmatically execute the app's functionality in Cytoscape (command line tool) and with external tools (e.g. R, Jupyter, Python, etc). This allows users to share their analysis workflows and therefore increase repeatability and reproducibility. Three use cases demonstrate automated workflows, combinations with other Cytoscape apps and core Cytoscape functionality. We first extend a protein-protein interaction network created with the stringApp, with compound-target interactions and disease-gene annotations. In the second use case, we created a workflow to load differentially expressed genes from an experimental dataset and extend it with gene-pathway associations. Lastly, we chose an example outside the biological domain and used CyTargetLinker to create an author-article-journal network for the five authors of this manuscript using a two-step extension mechanism. With 400 downloads per month in the last year and nearly 20,000 downloads in total, CyTargetLinker shows the adoption and relevance of the app in the field of network biology. In August 2019, the original publication was cited in 83 articles demonstrating the applicability in biomedical research

Academic drug discovery: Current status and prospects

Introduction. The contraction in pharmaceutical drug discovery operations in the past decade has been counter-balanced by a significant rise in the number of academic drug discovery groups. In addition, pharmaceutical companies that used to operate in completely independent, vertically-integrated operations for drug discovery are now collaborating more with each other, and with academic groups. We are in a new era of drug discovery. Areas Covered. This review provides an overview of the current status of academic drug discovery groups, their achievements and the challenges they face,, together with perspectives on ways to achieve improved outcomes. Expert Opinion. Academic groups have made important contributions to drug discovery, from its earliest days and continue to do so today. However, modern drug discovery and development is exceedingly complex, and has high failure rates, principally because human biology is complex and poorly understood. Academic drug discovery groups need to play to their strengths and not just copy what has gone before. However, there are lessons to be learnt from the experiences of the industrial drug discoverers and four areas are highlighted for attention: (i) increased validation of targets, (ii) elimination of false hits from HTS, (iii) increasing the quality of molecular probes and (iv) investing in a high quality informatics infrastructure

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors. View Full-TextThe APC was funded by IKERDATA, S.L. under grant 3/12/DP/2021/00102—Area 1: Development of innovative business projects, from Provincial Council of Vizcaya (BEAZ for the Creation of Innovative Business Innovative business ventures)

Towards a Conceptual Framework for Persistent Use: A Technical Plan to Achieve Semantic Interoperability within Electronic Health Record Systems

Semantic interoperability within the health care sector requires that patient data be fully available and shared without ambiguity across participating health facilities. Ongoing discussions to achieve interoperability within the health care industry continue to emphasize the need for healthcare facilities to successfully adopt and implement Electronic Health Record (EHR) systems. Reluctance by the healthcare industry to implement these EHRs for the purpose of achieving interoperability has led to the proposed research problem where it was determined that there is no existing single data standardization structure that can effectively share and interpret patient data within heterogeneous systems. \ \ The proposed research proposes a master data standardization and translation (MDST) model – XDataRDF -- which incorporates the use of the Resource Description Framework (RDF) that will allow for the seamless exchange of healthcare data among multiple facilities. Using RDF will allow multiple data models and vocabularies to be easily combined and interrelated within a single environment thereby reducing data definition ambiguity.

Htab2RDF: Mapping HTML Tables to RDF Triples

The Web has become a tremendously huge data source hidden under linked documents. A significant number of Web documents include HTML tables generated dynamically from relational databases. Often, there is no direct public access to the databases themselves. On the other hand, RDF (Resource Description Framework) gives an efficient mechanism to represent directly data on the Web based on a Web-scalable architecture for identification and interpretation of terms. This leads to the concept of Linked Data on the Web. To allow direct access to data on the Web as Linked Data, we propose in this paper an approach to transform HTML tables into RDF triples. It consists of three main phases: refining, pre-treatment and mapping. The whole process is assisted by a domain ontology and the WordNet lexical database. A tool called Htab2RDF has been implemented. Experiments have been carried out to evaluate and show efficiency of the proposed approach

Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.This research and the APC were funded by Consolidation and Structuring of Competitive Research Units—Competitive Reference Groups (ED431C 2018/49) funded by the Ministry of Education, University and Vocational Training of Xunta de Galicia endowed with EU FEDER funds

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems

Reply to: Soils need to be considered when assessing the impacts of land-use change on carbon sequestration

Industrial Ecolog

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds