AI in chemistry and beyond: Learning Chemical Intuition from Humans in the Loop
|Hour||15:15 › 16:15|
|Speaker||Oh-hyeon studied computational neuroscience at EPFL with Prof. Herzog, where she researched the fundamental aspects of computer vision models. She then started her career at Novartis focusing on machine learning research for drug discovery. Soon, she will start a new challenge at a med-tech company (SynpleChem) for lab automation.|
|Category||Conferences - Seminars|
The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very time-consuming process that typically spans many years within a chemist's career. In this work, we aim to replicate this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis. We exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization, motif rationalization, and biased de novo drug design.
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