AI for Molecular Discovery

How can we leverage the big data revolution to design new molecules, materials and drugs? Recent advancements in computer hardware and machine learning are helping experimentalists find new compounds in a matter of hours, instead of painstakingly carrying out thousands of experiments in a lab. We will explore how understanding the theoretical fundamentals of physical matter coupled with enough computational power can help us build models that predict the physical properties of molecules.

Each session will include an introductory lecture, followed by a hands-on programming workshop, where we will design machine learning models based on the most recent advancements in artificial intelligence.

You will find answers to these questions:

– What is artificial intelligence and machine learning?
– How can we simulate molecules inside a computer?
– How does understanding language help us predict the properties of molecules?
– What are the limitations of modelling compared to lab experiments?
– How can we use the above to find new drugs or materials?

The course would benefit students that have prior exposure to high-school chemistry, physics, biology, and Python programming. Depending on the experience, there will be time to go over introductory concepts and a lot of the code will be pre-written, so that we can focus on the science behind it all instead of writing many lines of code.

Jakub Lála

Jakub is a recent graduate of Materials Science and Engineering from Imperial College London. He is currently on a gap year before starting his PhD studies. During his undergraduate degree, Jakub discovered a passion for modelling materials, molecules, and proteins, particularly with the use of machine learning tools. In the summer of 2021, he had the opportunity to work in a research lab at EPFL in Lausanne, Switzerland and later that year, he was fortunate enough to visit MIT where he learned from some of the leading experts in artificial intelligence. He is now applying these new AI methods to various molecular problems in chemistry and biology. In his free time, he enjoys exploring philosophy and psychology, practicing Stoicism and Buddhism, reading, dancing, and producing music.

Session D

AI for Molecular Discovery

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