Training modules:
Course 01: Basics of Computer Aided Drug Discovery Part-I:
A perfect course for Bachelors / Masters / PhD students who are getting started into Drug Discovery research. This course is specially designed keeping in view of beginner level knowledge on computational drug discovery applications for science students. By the end of this course participants will be equipped with the basic knowledge required to navigate their drug discovery project making use of the biological databases and computational tools.
Here's what you'll learn from this course:
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Introduction to Computer Aided Drug Discovery
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Introduction to databases like PDB, PubChem and ZINC database
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How to visualize protein and ligands in Biovia Discovery Studio and MGLtools
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How to prepare files for docking studies.
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How to execute molecular docking.
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How to analyze the docking output results.
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How to generate publication quality figures from the docking output.
Course 02: Introduction to Applications of Artificial Intelligence & Machine Learning techniques in Drug Discovery, Designing & Development process:
A perfect course for Bachelors / Masters / PhD students who are getting started into Drug Discovery research. This course is specially designed keeping in view of beginner level knowledge on Artificial Intelligence, Machine learning and computational drug discovery applications for science students. By the end of this course participants will be equipped with the basic knowledge required to navigate their drug discovery project making use of the Artificial Intelligence and Machine learning based tools.
Here's what you'll learn from this course:
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Introduction to drug discovery and applications of AI & ML techniques at different stages.
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ABCDs of AI & ML terminology.
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Introduction to types of Artificial Intelligence & Machine learning.
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Introduction to Neural networks and Natural Language processing.
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Introduction to potential of AI & ML in Drug Discovery.
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Introduction to advantages and limitations of applying AI in drug discovery.
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Current Solution 1: AI based information aggregation from vast literature.
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Current Solution 2: AI based systems modelling to understand disease mechanisms.
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Current Solution 3: AI based systems modelling of novel drug like molecules.
Course 03: Virtual Screening for Drug Discovery:
Virtual Screening for Drug Discovery - Learn In-silico lead like drug discovery using virtual screening technique:
A perfect course for beginner level Bachelors / Masters / PhD students / scholars / researchers involved in In-silico drug discovery or enthusiasts interested in learning how to apply different types of virtual screening techniques for lead like drug compound identification against a drug target of interest. By the time you complete this course, you will be equipped with the knowledge required to execute virtual screening on your own starting from setting up the software to analyzing results.
Virtual screening (also referred as In-silico screening) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. The principles of virtual screening include measuring the presence or absence of specific substructures, matching certain calculated molecular properties, and fitting putative ligand molecules into the target receptor site. As the accuracy of the method has increased, virtual screening has become an integral part of the drug discovery process. This method can design and optimize various libraries from available compounds. According to the increased accuracy with decreased costs of this approach, in silico screening has now become an indispensable part of the drug discovery process.
Here's what you'll learn from this course:
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Introduction to Virtual Screening
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Introduction to different types of virtual screening
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How to download compound libraries from public databases
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How to prepare protein and compounds database for virtual screening
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How to execute virtual screening
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How to analyze virtual screening result.
Course 04: Molecular Dynamic Simulations for Drug Discovery:
A perfect course for Bachelors' / Masters' / PhD students who are getting started into computational drug discovery and aware of the In silico drug discovery basics. By the time you complete this course, you will be equipped with the knowledge required to execute molecular dynamic simulations on your own starting from setting up the software to analyzing results.
Here's what you'll learn from this course:
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Introduction to Molecular dynamic simulations
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What is OPLS force field
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How to setup the simulation for execution
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How to execute the simulation step-by-step
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How to analyze the simulation output
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How to generate and interpret the simulation output graphs.
Course 05: Leveraging Chat GPT for Drug Discovery Research:
This course is intended for researchers involved in drug discovery and aims to teach them how to use ChatGPT, a large language model, to improve their research process. The course will cover the basics of natural language processing (NLP), which is a field of artificial intelligence that focuses on the interaction between computers and human language.
Participants will learn about the capabilities of ChatGPT, which is a state-of-the-art language model that can understand and generate human-like language. They will also learn about the various applications of ChatGPT in drug discovery, such as predicting potential drug targets, identifying drug candidates, and assisting in drug repurposing efforts.
One of the main benefits of using ChatGPT in drug discovery is its ability to analyze vast amounts of data quickly and efficiently. This allows researchers to identify potential drug targets and drug candidates more efficiently and accurately than traditional methods.
By the end of the course, participants will have a good understanding of how to use ChatGPT to improve their drug discovery research. They will have learned about the basics of NLP, the capabilities of ChatGPT, and how to use it to accelerate the drug discovery process. They will also be able to apply this knowledge to their own research projects and contribute to the development of new and effective drugs.
Here's what you'll learn from this course:
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Introduction to Natural Language Processing.
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Techniques used in natural language processing.
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Applications of Natural Language Processing in Drug Discovery research.
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Introduction to ChatGPT.
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How ChatGPT can be used to predict potential drug targets.
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How ChatGPT can be used to assist in drug repurposing efforts.
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How ChatGPT can be useful in changing citation reference formats.
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How ChatGPT can help writing review from given citations.
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How ChatGPT can help finding citations for the review statements.
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Disadvantages & limitations of ChatGPT.
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