12-02, 16:30–17:00 (UTC), Talk Track I
This talk will go into how Deep Learning is changing the world of Cheminformatics. We will dive deep into how we can leverage traditional NLP Transformer models can enable us to performing a totally uncorrelated task such as Drug Discovery. This talk will give a brief introduction to the field of Cheminformatics and then go into detail as to how and what kind of Transformers can be utilized for the task at hand.
The Drug Discovery market is poised to be estimated at $75 Billion dollars by 2025 and the recent pandemic has taught us how crucial it is to be more agile in the process of Drug Discovery. Due to the recent advances in Deep Learning, Computational Drug Discovery has been possible with great execution speed and scale. The talk is outlined as:
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Background: This section will go in the relevant background required for understanding cheminformatics problems from a Machine Learning perspective as well as give an intuition into the importance of the problem.
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Deep Learning Methodologies: This section will dive into how the problem statements are formulated in a DL setup and how these are effectively tackled using the recent advancements in Graph Neural Networks and primarily Transformers (the focus of the talk).
No previous knowledge expected
I am a huge admired of Machine/Deep Learning and an Applied Scientist at Microsoft. I have been fascinated with the world of data and machine learning since I first learned about it and have been extremely fortunate enough to have worked alongside great people and projects including working at the top NeuroImaging lab in US at my alma matter USC as well as working on developing Drug Discovery Deep Learning models at NVIDIA.