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Abstract: ML has become a crucial tool in drug discovery and chemistry at large, e.g. to predict molecular properties, such as bioactivity, with high levels of accuracy. However, activity cliffs – pairs of molecules that are highly similar in their structure but exhibit large differences in potency – have been underinvestigated for their effect on model performance. Not only are these edge cases informative for molecule discovery and optimization, but models that are well-equipped to accurately predict the potency of activity cliffs have an increased potential for prospective applications...
Speaker: Derek van Tilborg - https://twitter.com/DerekvTilborg
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