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Instructor Name

Dr.Omics

Category

Internships

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Course Requirements

  • Motivation to Learn: A strong desire to engage with and understand the material.
  • Basic Knowledge: Familiarity with biology and molecular biology concepts.
  • Interest in Technology: Eagerness to learn about the latest technologies in Next-Generation Sequencing (NGS).
  • Software: Free software will be utilized, so no additional software purchases are necessary.
  • Hardware: A laptop with a minimum of 4GB RAM and 100GB of hard disk space.

Course Description

  • Bioinformatics Fundamentals: Explore databases (NCBI, PubChem), tools (BLAST, Mega), and Linux basics.
  • Computer-Aided Drug Design (CADD): Introduction to drug discovery, chemical structure visualization, and molecular biology fundamentals.
  • Molecular Modeling Techniques: Hands-on practice with molecular visualization tools like PyMOL and Chimera.
  • Chemical Informatics and Virtual Screening: Utilize chemical databases for data mining and virtual screening.
  • Machine Learning in Drug Design: Basics of machine learning, data preprocessing, and application in drug-target interaction prediction.
  • Real-World Application: Collaborate on a research project, from designing to publication, gaining practical experience in CADD.
  • HR Sessions: Resume building, interview prep, career development.
  • Course Outcomes

    • Gain proficiency in bioinformatics tools and databases.
    • Understand the fundamentals of drug design and computational methods.
    • Develop skills in molecular modeling and chemical informatics.
    • Master machine learning techniques for drug design applications.
    • Apply acquired knowledge to real-world research projects, collaborating with industry professionals.
    • Enhance professional development with HR sessions.

    Rules & Regulations

    • Attendance and Participation: Maintain a minimum of 75% attendance. Regular assessments and attendance contribute to performance evaluation.
    • Discipline: Maintain punctuality and respect in live classes. Engage actively and interact respectfully with instructors and peers.
    • Course Fee Payment: Pay course fees on time to avoid suspension or cancellation of access.
    • Assignments and Project: Complete assignments and projects sincerely and submit them on time.
    • Feedback and Communication: Maintain open communication with instructors and provide constructive feedback.
    • Certification: A certificate will be awarded upon course completion.
    • Paper Publication: eligibility requires a minimum 75% combined attendance and performance score.

    Course Curriculum DOWNLOAD BROCHURE

    1 Introduction to Bioinformatics
    1 Hour


    2 NCBI Database Overview
    1 Hour


    3 Genbank Database Practical Exercises
    1 Hour


    4 UCSC Genome Browser Overview
    1 Hour


    5 UCSC Genome Browser Hands-on Exercises
    1 Hour


    6 Pubmed Database Introduction
    1 Hour


    7 Clinvar Database Overview
    1 Hour


    8 KEGG Database Overview and Exercises
    1 Hour


    9 Protein Databases (UniProt)
    1 Hour


    10 Protein Databases (PDB)
    1 Hour


    11 Online BLAST Introduction and Exercises
    1 Hour


    12 Standalone BLAST Setup and Exercises
    1 Hour


    13 Standalone BLAST Advanced Exercises
    1 Hour


    14 Multiple Sequence Alignment with MEGA
    1 Hour


    1 Overview and Installation of Linux
    1 Hour


    2 Basic Linux Commands
    1 Hour


    3 Advanced Linux Commands
    1 Hour


    4 Package Management using Repository
    1 Hour


    1 Introduction to Python
    1 Hour


    2 Data Types
    1 Hour


    3 String Handling
    1 Hour


    4 Data Structure
    1 Hour


    5 Control Structure
    1 Hour


    6 Function
    1 Hour


    7 File Handling
    1 Hour


    8 Data Manipulation
    1 Hour


    9 Data Visualization
    1 Hour


    10 Biopython
    1 Hour


    1 Introduction and Installation of R
    1 Hour


    2 Data Types in R
    1 Hour


    3 Data Structure
    1 Hour


    4 File Handling
    1 Hour


    5 Control Structure
    1 Hour


    6 Function
    1 Hour


    7 Package Management
    1 Hour


    8 Data Manipulation
    1 Hour


    9 Data Visualization
    1 Hour


    10 Statistical Analysis
    1 Hour


    1 Introduction to Drug Discovery Process
    1 Hour


    2 Role of Computational Methods
    1 Hour


    3 Hands-on: Chemical Structure Visualization
    1 Hour


    4 Biomolecules and Their Properties
    1 Hour


    5 Structure of Proteins and Ligands
    1 Hour


    6 Hands-on: Protein Structure Visualization
    1 Hour


    7 Molecular Visualization Tools
    1 Hour


    8 Molecular Mechanics and Dynamics Simulations
    1 Hour


    9 Molecular Mechanics and Dynamics Simulations (continued)
    1 Hour


    10 Chemical Databases and Data Mining
    1 Hour


    11 Ligand and Structure-Based Virtual Screening
    1 Hour


    12 Hands-on: Chemical Data Exploration
    1 Hour


    13 Advanced Virtual Screening Techniques
    1 Hour


    14 Virtual Screening using Autodock Vina
    1 Hour


    15 Principles of Molecular Docking
    1 Hour


    16 Scoring Functions in Docking
    1 Hour


    17 Hands-on: Molecular Docking
    1 Hour


    18 Introduction to Molecular Dynamics
    1 Hour


    19 Simulation Software (e.g., GROMACS)
    1 Hour


    20 Hands-on: Analyzing MD Data
    1 Hour


    21 Chemoinformatics: Data Analysis and Visualization
    1 Hour


    22 Protein-Ligand Interaction Analysis
    1 Hour


    23 Hands-on Protein-Ligand Interaction Analysis
    1 Hour


    24 Pharmacophore Modeling and Applications
    1 Hour


    25 Chemoinformatics: Data Analysis and Visualization (continued)
    1 Hour


    26 Structure-Based Drug Design
    1 Hour


    27 Ligand-Based Drug Design
    1 Hour


    28 Hands-on Structure-Based and Ligand-Based Drug Design
    1 Hour


    29 ADMET in Drug Development
    1 Hour


    30 Course Conclusion
    1 Hour


    1 Basics of Machine Learning
    1 Hour


    2 Supervised, Unsupervised, and Reinforcement Learning
    1 Hour


    3 Hands-on: Learn the Basics with scikit-learn Library in Python
    1 Hour


    4 Data Cleaning and Feature Selection
    1 Hour


    5 Handling Molecular Data
    1 Hour


    6 Hands-on: Use Pandas and NumPy for Data Preprocessing
    1 Hour


    7 Regression and Classification Algorithms
    1 Hour


    8 Deep Learning in Drug Discovery
    1 Hour


    9 Hands-on: Implement Machine Learning Models using scikit-learn
    1 Hour


    10 Hands-on: Implement Machine Learning Models using TensorFlow/Keras
    1 Hour


    11 Predicting Drug-Target Interactions
    1 Hour


    12 QSAR Modeling
    1 Hour


    13 Hands-on: Apply Machine Learning to Real Datasets with RDKit
    1 Hour


    14 Hands-on: Apply Machine Learning to Real Datasets with Cheminformatics
    1 Hour


    15 Structure-Activity Relationship (SAR) Analysis
    1 Hour


    16 Hands-on: Use RDKit for SAR Analysis
    1 Hour


    17 De Novo Drug Design using ML
    1 Hour


    18 Explore De Novo Design Tools
    1 Hour


    19 Advanced Machine Learning Techniques in Drug Design
    1 Hour


    20 Integration of Omics Data in Drug Discovery
    1 Hour


    21 Clinical Trial Design and Data Analysis
    1 Hour


    22 Ethical Considerations in Drug Design and Machine Learning
    1 Hour


    23 Real-World Applications
    1 Hour


    24 Q&A and discussion
    1 Hour


    25 Conclusion
    1 Hour


    Student Feedback

    CADD INTERNSHIP ( 6 MONTHS )

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