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.
Course Outcomes
- 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.
Course Format
- 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.
Last Date for Registration : 14 March 2022
Date of First Lecture : 16 March 2022
Instructors
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
Course Content
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.
Tutorial Content
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.
Queries ?
contact.ml4chem@ihub-data.iiit.ac.in
Participants
| Abhijit Patra | Ketkar Uttara Yogesh | Saket Kumar |
| Aditi Pradhan | Koka Prabhu Sathwik | Sandeep Nagar |
| Aditi Singh | Komal Yadav | Sandip Giri |
| Aditya Tripathi | Korlepara Divya Bharathi | Sangram Keshari Patro |
| Agnibha Nandi | Koushik Kasavajhala | Sanika Joshi |
| Ajay Manaithiya | Krishna Chandra Mardi | Sanjana Pandey |
| Akash Deep | Krishnendu Sinha | Satyendra Rajput |
| Akshat Sharma | Lakshmi Devi Voleti | Sayan Dutta |
| Ananya Singhal | M Vanitha | Shaik Salma Sony |
| Aniruddha Seal | M. Lakshmi Prabha | Shaurya Anand |
| Anish Gomatam | Manasmit Jena | Shayandeep Bhaumik |
| Anju Cyriac | Manender Yadav | Sheeba Malik |
| Anoushka Sachdeva | Manila Boipai | Shivam Pandit |
| Anshika Dhiman | Maturi Renuka | Shivam Rawat |
| Ashashree Sahoo | Mckenna Buckley | Shivam Sharma |
| Ashish Kumar | Megha Hada | Shreyansh Shukla |
| Ashish Meena | Mitul Bhalerao | Shriraj Hegde |
| Ashutosh Bhuyan | Mohammad Shueb | Shrishti Barethiya |
| Ashwini Babu | Mohit Virdi | Shriya Rajan Deshpande |
| Athira C A | Muhseen | Shruti Jeurkar |
| Avik Das | Naman Mishra | Shubham Sharma |
| Avinash Garg | Neelima P V | Shweta Kumari |
| Bodhayan Biswas | Nikhil Kumar | Sibasankar Panigrahy |
| Boyli Ghosh | Oshiya R A | Siddharth Sankar Dutta |
| Catherine Ghosh | Pallavi Vanjari | Soham Choudhuri |
| Chandan Kumar Das | Paritosh Singh | Soumya Shaswati Sahu |
| Chandra Chowdhury Sarkar | Partha Mondal | Srinivas Hotha |
| Chandrashekhar Iyyer | Parul | Subhojit |
| Choudhary Bharat Swaroopram | Prachi Chauhan | Suyash Gupta |
| Desu Surya Sai Teja | Pradeep Kumar Pal | Swapan Ghosh |
| Dibyendu Maity | Prasad K Mohite | Swarnawa Mitra |
| Dishan Das | Praseetha Prakash | Swati Sudipta Sahoo |
| Dr. Moumita Majumder | Prashant Kumar | Tanaya Dutta |
| Dulari Hakamuwa Lekamlage | Prateek Malhotra | Tanveer Tadavi |
| Durgeshwari Rathore | Prathit Chatterjee | Tharun Chand |
| Gangarapu Kiran | Preeti Chauhan | U. Divya Madhuri |
| Gaurav Agarwal | Prem Sai Balaji Peddineni | Uday Sankar M |
| Gaurav Dahiya | Prithwitosh Dey | Udit Srivastava |
| Harkishan Dua | Priyanshu Singh | Utsav Dey Sarkar |
| Harsha Satya Vardhan | Rahul Hooda | Vaidehi Yuvraj Rathod |
| Himaja Devarakonda | Rajith K Ravindren | Vajiha Hussain |
| Hrithik Gudapati | Rakesh Srivastava | Vasavi C.S |
| Indraneel Dhavale | Reena | Vatsal Trivedi |
| Jadhav Ganesh Radhakisan | Rifat Hashim | Venkata Sai Sreyas Adury |
| Jay Sonekar | Rishabh Verma | Vidit Agarwal |
| Jayashree Biswal | Rituparna Roy | Vidya Sagar Jerra |
| Jeni Anna Mathew | Rohan Kumar | Vikram Sunil Gaikwad |
| John C Sunil | Rohit Modee | Vishal |
| Jovan Jose K V | Roja.E | Vishal Kaleeswaran |
| Kalavadia Malay | Rupesh Chikhale | Vishal Kumar |
| Kameshwar Prasad | Rushikesh Kale | Vishnupriya Bhakthavatsalam |
| Karthik Nayak | S.I Aadharsh Raj | Viswanadh Kanamarlapudi |
| Karuna Anna Sajeevan | Saalim Hassan Raza | Yashraj |
| Kesana Veekshitha | Saikat Dhibar | Yenugu Nikhil |
| Kethavath Venkatesh | Saikat Dutta Chowdhury | Yogesh Vishnu Sutar |
| Zachariah Schuurs | ||
Program Coordinator:
Teaching Assistants:
