Advanced Certificate in Structural Bioinformatics: Drug Discovery
-- ViewingNowThe Advanced Certificate in Structural Bioinformatics: Drug Discovery is a comprehensive course designed to equip learners with essential skills in the field of bioinformatics and drug discovery. This course emphasizes the importance of understanding the 3D structures of biological macromolecules and their interactions, which is crucial for the development of novel therapeutic strategies.
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โข Advanced Bioinformatics Algorithms: This unit will cover the advanced algorithms and data structures that are commonly used in structural bioinformatics and drug discovery, including graph algorithms, machine learning, and data mining techniques.
โข Protein Structure Prediction: This unit will focus on the methods and tools used to predict protein structure, including homology modeling, ab initio methods, and molecular dynamics simulations.
โข Molecular Docking and Virtual Screening: This unit will cover the principles and applications of molecular docking and virtual screening in drug discovery, including ligand-protein interactions, scoring functions, and lead optimization.
โข Pharmacophore Modeling and 3D Quantitative Structure-Activity Relationship (3D-QSAR): This unit will explore the concepts and techniques used in pharmacophore modeling and 3D-QSAR, including feature-based methods, comparative molecular field analysis, and machine learning algorithms.
โข Systems Biology and Network Analysis: This unit will introduce the principles of systems biology and network analysis in drug discovery, including protein-protein interaction networks, gene regulatory networks, and pathway analysis.
โข Biological Data Management and Analysis: This unit will cover the best practices and tools for managing and analyzing large-scale biological data, including databases, data visualization, and statistical methods.
โข Computational Toxicology: This unit will explore the application of computational methods in toxicology, including predictive toxicology, read-across, and in silico methods for hazard assessment.
โข Regulatory and Ethical Considerations in Drug Discovery: This unit will discuss the ethical and regulatory considerations in drug discovery, including data privacy, intellectual property, and regulatory compliance.
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