scholarly journals Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 908 ◽  
Author(s):  
Aurora S. Blucher ◽  
Shannon K. McWeeney ◽  
Lincoln Stein ◽  
Guanming Wu
Keyword(s):  
Targeted Therapies ◽  
Biological Networks ◽  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Patient Specific ◽  
Functional Interaction ◽  
Functional Interaction Network ◽  
A Genome

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

scholarly journals Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Gaston K. Mazandu ◽  
Nicola J. Mulder
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Targets ◽  
Large Scale ◽  
Global Analysis ◽  
Interaction Network ◽  
Interaction Networks ◽  
Microbial Pathogens ◽  
Potential Drug ◽  
Functional Interaction ◽  
Potential Drug Targets

Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.

scholarly journals DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

2021 ◽  
Author(s):  
Tilman Hinnerichs ◽  
Robert Hoehndorf
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Computational Method ◽  
Interaction Networks ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Link Type ◽  
Molecular Features

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.

Targeted agents recommended by the CNS TAP tool compared to those selected by a tumor board in a molecularly-driven clinical trial in children and young adults with DIPG.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 2048-2048
Author(s):  
Holly Roberts ◽  
Karthik Ravi ◽  
Cassie Kline ◽  
Sabine Mueller ◽  
Carl Johannes Koschmann ◽  
...  
Keyword(s):  
Anticancer Agents ◽  
Targeted Therapies ◽  
Treatment Plan ◽  
Study Participant ◽  
Genetic Alterations ◽  
Tumor Board ◽  
Patient Specific ◽  
Targeted Agents ◽  
Genetic Sequencing ◽  
Molecular Tumor Board

2048 Background: Genetic sequencing of diffuse intrinsic pontine glioma (DIPG) and diffuse midline glioma (DMG) biopsy specimens has revealed genomic heterogeneity, fueling an interest in individualized, targeted treatment options. The Pacific Pediatric Neuro-Oncology Consortium recently completed a feasibility study PNOC003: Molecular Profiling for Individualized Treatment Plan for DIPG (NCT02274987), in which a multidisciplinary tumor board recommended targeted agents based on the molecular and genetic profiling of each patient’s tumor. Separately, our group developed a numeric scoring tool of targeted anticancer agents, the Central Nervous System Targeted Agent Prediction (CNS TAP) tool, which combines pre-clinical, clinical, and CNS penetration data with patient-specific genomic information to generate a numeric score for each agent to objectively evaluate these targeted therapies for use in patients with CNS tumors. We hypothesized that highly-scored agents within the CNS-TAP tool would overlap, at least in part, with the agents recommended by the molecular tumor board in PNOC003. Methods: For each study participant (n=28), a retrospective analysis was completed, utilizing the genomic report to identify actionable genetic alterations and to input patient-specific data into CNS TAP to identify the highest scoring agents. We compared high-scoring agents within the CNS TAP tool with recommendations from the PNOC003 tumor board for each of the enrolled 28 patients. Results: Overall, 93% (26/28) of patients had at least one agent recommended by both the tumor board and CNS TAP. Additionally, 39% (37/95) of all agents recommended by the tumor board were also selected by CNS TAP, with additional analysis ongoing. Conclusions: There was significant overlap between the highest-scoring and selected agents via CNS TAP compared with those chose by the molecular tumor board. Through this work, we also identified factors that likely contributed to the discordance in choice of targeted therapies. Without clinician input, the CNS TAP tool is unable to account for drug-drug interactions, includes only designated anticancer agents, and cannot easily be updated in real time, requiring extensive manual literature review for each included agent. However, CNS TAP provides an objective evaluation of targeted therapies, in contrast to inherently subjective recommendations of a tumor board. Given the discordance identified between these methods and the strengths of each, a prospective study incorporating both CNS TAP and a molecular tumor board for targeted therapy selection in patients with high grade glioma is warranted.

scholarly journals QPowered Compound2DeNovoDrugPropMax –A Novel Programmatic Tool Incorporating Deep Learning and In Silico Methods for Automated In Silico Bio- Activity Discovery for any Compound of Interest

2021 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Interaction Network ◽  
Network Data ◽  
Data Set ◽  
Target Interaction

Network data is composed of nodes and edges. Successful application of machine learning/deep<br>learning algorithms on network data to make node classification and link prediction have been shown<br>in the area of social networks through which highly customized suggestions are offered to social<br>network users. Similarly one can attempt the use of machine learning/deep learning algorithms on<br>biological network data to generate predictions of scientific usefulness. In the presented work,<br>compound-drug target interaction network data set from bindingDB has been used to train deep<br>learning neural network and a multi class classification has been implemented to classify PubChem<br>compound queried by the user into class labels of PBD IDs. This way target interaction prediction for<br>PubChem compounds is carried out using deep learning. The user is required to input the PubChem<br>Compound ID (CID) of the compound the user wishes to gain information about its predicted<br>biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for<br>the input CID. Further the tool also optimizes the compound of interest of the user toward drug<br>likeness properties through a deep learning based structure optimization with a deep learning based<br>drug likeness optimization protocol. The tool also incorporates a feature to perform automated In<br>Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand<br>interaction profiles. The program is hosted, supported and maintained at the following GitHub<br><div>repository</div><div><br></div><div>https://github.com/bengeof/Compound2DeNovoDrugPropMax</div><div><br></div>Anticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use<br>the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep<br>learning models into a quantum layer and introduce quantum layers into classical models to produce a<br>quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the<br><div>same is provided below</div><div><br></div>https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax<br>

scholarly journals Network Insights into Improving Drug Target Inference Algorithms

2020 ◽  
Author(s):  
Muying Wang ◽  
Heeju Noh ◽  
Ericka Mochan ◽  
Jason E. Shoemaker
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Protein Interactions ◽  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Expression Data ◽  
Protein Protein Interactions ◽  
Cell Tissue ◽  
Inference Algorithms

AbstractTo improve the efficacy of drug research and development (R&D), a better understanding of drug mechanisms of action (MoA) is needed to improve drug discovery. Computational algorithms, such as ProTINA, that integrate protein-protein interactions (PPIs), protein-gene interactions (PGIs) and gene expression data have shown promising performance on drug target inference. In this work, we evaluated how network and gene expression data affect ProTINA’s accuracy. Network data predominantly determines the accuracy of ProTINA instead of gene expression, while the size of an interaction network or selecting cell/tissue-specific networks have limited effects on the accuracy. However, we found that protein network betweenness values showed high accuracy in predicting drug targets. Therefore, we suggested a new algorithm, TREAP (https://github.com/ImmuSystems-Lab/TREAP), that combines betweenness values and adjusted p-values for target inference. This algorithm has resulted in higher accuracy than ProTINA using the same datasets.

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