Machine Learning for Chemistry and Drug Design
With AI/ML revolutionizing the field of Sciences, there is an increasing need for professionals having knowledge in both the domains. iHub-Data in collaboration with IIIT-H is offering a unique course on “Machine Learning for Chemistry and Drug Design”, with an emphasis on drug discovery. The course features theoretical lectures by eminent faculty from the fields of Computer Science and Natural Sciences. It also includes a programming tutorial component to help develop practical skills. The course is ideally suited for students and researchers who may want to develop interdisciplinary skills in solving computationally intensive problems involving natural sciences.
Who can participate?
- Students, Researchers, Professionals with background in sciences who wish to understand methods of AI/ML in chemistry and biology.
- Students and professions with background in computer science who wish to understand applications of AI/ML in Sciences.
- The course is restricted to Indian Nationals
- As Prerequisites for tutorials : An exposure to any programming language (or a keen interest to learn programming skills) would be highly beneficial.
What does the course offer?
- Fundamental theoretical and practical concepts in AI/ML and their application for Drug Discovery.
- Lectures covering topics in both Sciences and AI/ML to help you build a strong theoretical foundation.
- Hands-on programming tutorial sessions to help you gain practical application abilities.
What is the qualifying criteria?
- The course is open to students, researchers and working professionals willing to build theoretical and practical foundations for applications of AI in drug discovery.
- Participants are expected to have basic programming experience in any of the languages. Prior experience in Python is recommended.
- Background in Mathematics (up to class 12) is deemed necessary.
- Acquire knowledge of modern machine learning and deep learning methods.
- Understand important problems in drug discovery that AI can address.
- Get hands on experience on using various tools, libraries (such as Python, Pytorch, Scikit learn, numpy, pandas) for various machine learning and deep learning methods.
- At the end of this course, you would acquire the ability to approach novel problems in Science with AI/ML.
What makes this program unique?
- One of its kind, 12-week certificate program, offered in collaboration with IIIT-H faculty, with a strong emphasis on applications of AI/ML in drug discovery.
- Learn from India’s finest researchers and eminent faculty in both of CS and Sciences.
- Internships/Research opportunities for top performing Master’s, Ph.D students.
- Opportunity to collaborate with ML experts in your project/thesis.
- 90-minute weekly lectures by faculty to teach fundamental theoretical concepts.
- 150-minute weekly hands-on programming tutorial sessions to help gain practical experience.
- Weekly/Bi-weekly assignments to help explore beyond tutorial sessions.
- Regular feedback on assignments.
- Weekly office-hours with Teaching Assistants for one-on-one interaction and doubt clearance.
- The participation fee for this 12-week course is Rs 7,500 (for UG and Masters students), Rs 15,000 (for Ph.D students), Rs 20,000 (for Professors) and Rs 30,000 (for industry professionals).
- Female UG students can avail the course at a 30% waiver at Rs. 5250/-
- Partial waivers are available for students from underrepresented communities on a need/merit basis. Partial waivers may also be available for select Ph.D students without funding. Send an email to firstname.lastname@example.org for consideration.
To participate, please fill up the Registration form. After registration, please proceed to Payment
Last Date for Registration : 14 March 2022
Date of First Lecture : 16 March 2022
Prof. Deva Priyakumar, IIIT Hyderabad
Prof. C. V. Jawahar, IIIT Hyderabad
Prof. Bapi Raju, IIIT Hyderabad
Prof. Girish Verma, IIIT Hyderabad
Dr. Maitreya Maity, IHub-Data
Dr. Charu Sharma, IIIT Hyderabad
Introduction to the course
Introduction to Machine Learning (Part 1)
Introduction to Machine Learning (Part 2)
Machine Learning for Chemistry
Molecular feature vectors
Introduction to Deep Learning
Deep Learning – 2
Deep Learning – 3
Introduction to RL and molecular generation
Deep Learning Chemistry. – Case studies
Introduction to Autoencoders; Autoencoders for chemistry.
Introduction to molecular graphs and deep learning on Graphs.
Closing notes and Discussion.
Introduction to Python and Programming refresher.
Using libraries in Python(numpy, pandas, matplotlib) and EDA.
Building ML workflows with Scikit learn and Python.
Using Scikit learn for applications in chemistry.
Building molecular feature vectors in Python.
Introduction to Pytorch and pytorch for chemistry.
Pytorch for specific chemistry tasks.
Building CNNs and RNNs for chemistry tasks.
Graph Neural Networks in Pytorch.
Molecule generation with Python and RL.
Autoencoders with Pytorch and applications to chemistry.
Building a complete drug discovery pipeline using Pytorch.
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