Vanhaelen Computational Methods for Drug Repurposing

Erscheinungsjahr 2018
ISBN: 978-1-4939-8955-3
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1903, 332 Seiten, eBook

Reihe: Methods in Molecular Biology

This detailed book explores techniques commonly used for research into drug repurposing, a well-known strategy to find alternative indications for drugs which have already undergone toxicology and pharma-kinetic studies but have failed later stages during the development, via computational methods. Thereby, it addresses the intense challenges of identifying the appropriate type of algorithm and relevant technical information for computational repurposing. Written for the highly successful Methods in Molecular Biology series, the authors of each chapter use their experience in the field to describe the implementation and successful use of a specific repurposing method thus providing lab-ready instruction.  Authoritative and practical, Computational Methods for Drug Repurposing serves as an ideal guide to researchers interested in this vital area of drug development.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Vanhaelen, Quentin

Inhaltsverzeichnis

Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing.- Performing an In Silico Repurposing of Existing Drugs by Combining Virtual Screening and Molecular Dynamics Simulation.- Repurposing Drugs Based on Evolutionary Relationships between Targets of Approved Drugs and Proteins of Interest.- Drug Repositioning by Mining Adverse Event Data in ClinicalTrials.gov.- Transcriptomic Data Mining and Repurposing for Computational Drug Discovery.- Network-Based Drug-Repositioning: Approaches, Resources, and Research Directions.- A Computational Bipartite-Graph-Based Drug Repurposing Method.- Implementation of a Pipeline Using Disease-Disease Associations for Computational Drug Repurposing.- An Application of Computational Drug Repurposing Based on Transcriptomic Signatures.- Drug-Induced Expression-Based Computational Repurposing of Small Molecules Affecting Transcription Factor Activity.- A Drug Repurposing Method Based on Drug-DrugInteraction Networks and Using Energy Model Layouts.- Integrating Biological Networks for Drug Target Prediction and Prioritization.- Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing.- Computational Prediction of Drug-Target Interactions via Ensemble Learning.- A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.- Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities.- A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources.- Heter-LP: A Heterogeneous Label Propagation Method for Drug Repositioning.- Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.