Active machine learning for novel molecule discovery

Large-scale screening of new molecules is expensive and time-consuming. In the first part of the talk, I will introduce a novel active machine learning-based framework that statistically minimizes the number of wet-lab experiments needed to design new Antimicrobial Peptides (AMPs), while ensuring a high diversity and novelty of generated AMPs sequences, in multi-rounds of wet-lab AMP screening settings. To enhance the active learning, a powerful and robust uncertainty estimation method is needed. In the second part of the talk, a novel generative flow network-based dropout method called “GFlowOut” will be introduced and how it can help uncertainty estimation and active learning will be explained.