I agree with you. I transited from computing to drug discovery. A lot of projects attempt to harness the power of ML but there still seem to be a lot of misunde

I agree with you. I transited from computing to drug discovery. A lot of projects attempt to harness the power of ML but there still seem to be a lot of misunderstanding. A lot of the time the valuable ML methods are supporting / assisting hypothesis generation rather than 'doing it all'. The domain experts will judge the value of ML methods by their usual merits in the drug discovery world. If the more sophisticated methods can predict hit compounds no better than the traditional standard deviation based cut off, or if the models can not explain key insight of any indication of mechanism of action, then it may not be a good method after all. But meanwhile if it opens up a new way (e.g a new phenotype based screening) then it is worth pursuing.