Newcomer to learn AI in Biologics Drug Discovery

Introductions
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1

New comer here through Coursera. I would like to gain some hands-on experience using AI for Protein Drugs Discovery. Any suggestion how to get started would be super appreciated!

2

I’m newcomer too. best wishes for this course and new journey.

3

Have you done any courses about Machine Learning at all?

4

No, I haven’t that’s why I am anxious about it and its overwhelmingly.

5

Any knowledge in Python programming and/or mathematics and statistics at all?

6

I have done basic mathematics and statistics in graduation but no knowledge of Python programming.

7

@aima_shams90, you have taken over the Introduction thread of another learner.

Please start your own Introduction.

8

This depends on your level of programming skills and your mathematics background.

Can you give a bit more information on that?

9

Sure!

10

Welcome to the community, and it’s great that you’re diving into the exciting field of AI for Protein Drug Discovery! This is such a fascinating area with a lot of opportunities to make a real impact.

Here’s a roadmap to help you get started:

1. Strengthen Your Foundations in AI and Bioinformatics

  • Coursera Courses: Since you’re already on Coursera, there are some amazing courses that provide a solid foundation. Look for courses like:
    • AI for Medicine (by deeplearning.ai)
    • Bioinformatics (by UC San Diego)
    • Fundamentals of Bioinformatics (by the University of California, San Diego)
    • Deep Learning Specialization (by deeplearning.ai) — especially if you’re looking to dive deep into machine learning techniques for protein folding, drug binding, etc.

These will give you a good grounding in both the technical AI aspects and biological concepts needed for drug discovery.

2. Get Hands-On with AI Tools for Drug Discovery

  • DeepChem: A great open-source library designed specifically for bioinformatics and drug discovery, including protein-ligand interactions, molecular modeling, and prediction tasks.
  • PyMOL: Essential for visualizing protein structures, and combining it with AI can help in predicting how proteins fold and interact with drug molecules.
  • RDKit: Useful for cheminformatics — helps you manipulate chemical structures and build predictive models.
  • OpenAI’s GPT-3: While not directly related to proteins, it can help you with natural language processing tasks in bioinformatics, like literature mining or data interpretation.

3. Understand Protein Structure and Drug-Target Interactions

  • Protein Folding: AI-driven tools like AlphaFold by DeepMind have made incredible strides in protein structure prediction. It’s essential to understand how proteins fold and the role of structural biology in drug discovery.
  • Ligand Binding: Learning about docking simulations and binding affinity predictions will be key in understanding how drugs interact with target proteins.
  • Bioinformatics Databases: Familiarize yourself with resources like PDB (Protein Data Bank) for protein structures and PubChem for drug compounds.

4. Learn from Real-World Case Studies

  • Explore case studies where AI has been successfully applied to protein drug discovery. Companies like Insilico Medicine, Ardigen, and Atomwise are already doing pioneering work in this space. Their research papers or blog posts can be incredibly informative for your learning.

5. Collaborate and Join Communities

  • GitHub: Contribute to open-source projects related to drug discovery. This will provide you with practical experience.
  • Kaggle: Participate in bioinformatics competitions. There are plenty of challenges that combine AI and drug discovery (e.g., predicting bioactivity of compounds).
  • Forums/Meetups: Join forums like Bioinformatics Stack Exchange and AI-related Discord communities. Networking with professionals in the field and asking questions can open up new opportunities.

6. Experiment and Build Your Own Projects

  • Once you’re comfortable with the tools, start building projects like:
    • Predicting drug efficacy using AI models
    • Protein-ligand docking prediction
    • Machine learning models to identify novel drug candidates

Building your portfolio will help you gain hands-on experience and impress potential collaborators or employers!

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