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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom	Video and Text Based	Weekends

Course Overview

Computational Drug Discovery certification is a joint offering by Edu Plus Now and the Indian Statistical Institute, Pune. This certification course is conducted for 2 months during the weekends and is spread over a mere 48 hours only. The board member who gives advice for the course curriculum is from companies like Oracle, ISI, Suzlon, and others. The people who impart this course are faulty, and experts belonging to the field of computational drug discovery.

Computational Drug Discovery training will give me knowledge on drug-likeness analysis, in-silico pharmacokinetic, and computational toxicity. All these approaches can definitely provide help to make the discovery of drugs that are highly demanded in industries like the pharmaceutical industry. The course is laid with a mix of modules involving videos, lectures, discussions, and assignments that may be used in the future after the course subsides.

The Highlights

2 months programme
48 hours online course
Live interactive classes
Weekend sessions are held
Industry-endorsed curriculum
Certificate of completion

Programme Offerings

48 Hours Course
Live Online Course
Industry Experts Teaching
Industry Endorsed Curriculum
Lectures
Recordings
Projects
placement assistance

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	edu plus now

The Computational Drug Discovery certification is Rs. 30,000 with GST as additional charges.

Computational Drug Discovery Fee Structure

Description	Amount
Programme Fee	Rs. 30,000 + GST

Eligibility Criteria

Educational Qualification

Graduates who have 50% marks or more in their graduation are allowed.
Similarly who are postgraduate, or doctorate students are allowed.

Work Experience

Working professionals that have some association with the biological, pharmaceutical, industries may be preferred.

Certification Qualifying Details

The Computational Drug Discovery certification by eduplusnow, and Indian Statistical Institute, Pune will be given at the course end.

What you will learn

Knowledge of Pharmaceuticals

Computational Drug Discovery certification syllabus will guide on topics like strategies of design related to ligand- and receptor-based drug that usually includes 3D QSAR, 2D QSAR, molecular fingerprints, pharmacophore, QSAR-based database screening, small molecular file formats, pharmacophore-based virtual screening, and much more.

Who it is for

Here are people that can pursue this course:

Research scholars, especially doctoral or postdoctoral, are welcome.
Freshly passed out graduates who want to understand computational drug discovery.
In the areas of pharmaceutical, biological, and chemical fields, people who are working as researchers.
Teachers who are associated with teaching in the above fields.
People who want to make a contribution towards drug discovery.

Admission Details

Here are the Computational Drug Discovery admission details:

Step 1: Visit the official website: https://www.eduplusnow.com/course-details/computational-drug-discovery.

Step 2: Find the ‘Enroll Now’ button on the website homepage to click it.

Step 3: This button takes the user for making a registration on the website through a small registration form.

Step 4: After the registration process is completed, the next step is fee payment.

Step 5: As the fee is submitted, candidates get secured admission.

The Syllabus

Theoretical modules
Basic concept
Ligand-based drug design
- Quantitative Structure-Activity Relationship (QSAR)
- Two-dimensional QSAR (2D-QSAR)
- Pharmacophore space modelling
Structure-based drug design
- Molecular docking
Virtual screening
De novo drug design
Basics on artificial intelligence in drug design

PaDel
Pymol
Chimera
Autodock
Autodock Vina
MS Excel
OpenBabel
Coralsea
OpenQSAR
Etc.

Introduction to drug discovery.
Small molecule.
Molecular file format. (SDF, SMILES, PDB, MOL, MOL2, XYZ, PDBQT, etc.)
Drug and promising molecules.
Role of computers in drug discovery.
The basic concept of ligand-based drug design.
The basic concept of structure-based drug design.

Concept of small molecular datasets.
Concept of biological activity.
Basic introduction of chemometric methods.
Multiple Linear Regression | Partial Least-Square
Genetic algorithm | Principal Component Analysis.
Introduction of training and test sets.
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR)
- Concept of QSAR.
- History of QSAR.
- Objectives of QSAR. QSAR and regulatory perspectives.
- Molecular descriptors -
- 2D-Descriptors | 3D-Descriptors
- 4D-Descriptors | Topological Descriptors
TWO-DIMENSIONAL QSAR (2D-QSAR)
- Free-Wilson model.
- Fujita-Ban model.
- LFER Approach of Hansch model.
- The Mixed Approach.
- Application of 2D-QSAR models.
- Design of new molecules from 2D-QSAR.
THREE-DIMENSIONAL QSAR (3D-QSAR)
- The basic concept of 3D-QSAR.
- 3D-Descriptors.
- 3D-QSAR chemometric methods.
- Concept of 3D molecular fields.
- 3D-QSAR methodologies.
- Application of 3D-QSAR models.
- Design of new molecules from 3D-QSAR
PHARMACOPHORE SPACE MODELLING
- Introduction to pharmacophore.
- Pharmacophoric features.
- Training and test sets for pharmacophore.
- Pharmacophore algorithms.
- Pharmacophore model optimization.
- Application of pharmacophore models.
- Design of new molecules from pharmacophore models.

Introduction to receptor-based drug design.
Concept of the macromolecular target.
Concept of active site.
Macromolecule preparation.
Small molecule preparation.
MOLECULAR DOCKING
- Introduction of molecular docking.
- Introduction of rigid docking.
- Introduction of flexible docking.
- Types of molecular docking.
- Macromolecule-small molecule docking.
- Protein-ligand docking.
- DNA-ligand docking.
- Macromolecule-macromolecule docking. -
  - Protein-protein docking
  - Protein-DNA docking
  - DNA-DNA docking
  - Concept of grid
  - Concept of pose
  - Concept of dock score
- Selection of correct pose

Introduction to virtual screening.
Virtual screening and high-throughput screening (HTS).
Importance of virtual screening.
Introduction to small molecular databases.
- Asinex | ZINC | Maybridge | Mcule | Enamine | PubChem | NCI
- Concept of hits. | Concept of screening criteria.
Concept of hits to lead.
LIGAND-BASED VIRTUAL SCREENING
- Screening molecular databases based on 2D-QSAR. | Screening molecular databases based on 3D-QSAR.
- Screening molecular databases based on the pharmacophore model.
STRUCTURE-BASED VIRTUAL SCREENING
- Molecular docking of large chemical databases. | Setup of screening criteria. | Best molecule selection.

Introduction to de novo drug design.
De novo design algorithms. | Steps of de novo design.
Final molecules selections through de novo design.
Differentiate between de novo design and virtual screening.

Introduction to artificial intelligence.
Introduction to machine learning approaches.
Application of machine learning approaches in drug discovery.

Collection of molecular datasets from literature and databases.
Generation of 2D-descriptors.
Normalization of molecular descriptors.
Development of 2D-QSAR models.
Validation of 2D-QSAR models.
- Internal validation | Test set validation | Cross-validation
Interpretation of 2D-QSAR model.

Collection of molecular datasets from literature and databases.
Generation of molecular fields.
Development of 3D-QSAR models.
Validation of 3D-QSAR models.
- Internal validation | Test set validation | Cross-validation
Interpretation of 3D-QSAR model.

Collection of molecular datasets from literature and databases.
Exploration of pharmacophoric features.
Development of pharmacophore models.
Validation of pharmacophore models.
Interpretation of pharmacophore models.

Collection of target molecules from Protein Data Bank (PDB).
Repair and preparation of molecular target. | Preparation of ligands.
Active site identification.
- Co-crystal ligand.
- Pocket identification using several tools.
Grid generation.
Molecular docking.
Analysis of molecular docking.
Binding interactions analysis.
Optimization of the best-docked pose.