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CADD RESEARCH ORIENTED COURSE ( 6 MONTHS )

Instructor Name

Dr.Omics
Category

Research Oriented Courses

Reviews

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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 ClustalW
1 Hour

15 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 Hands-on: Molecular Docking
1 Hour

17 Introduction to Molecular Dynamics
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

Instructor

Dr.Omics

0 Rating
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213 Students
44 Courses

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This course includes:

  • Live classes

  • For full fee and payment plan details, please contact us at 9310870544

  • Duration 6 Months

  • Skill Level Advanced

  • Lectures 94 lessons

  • Full lifetime access

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