It's also the case that preclinical candidates developed this way are even harder to translate into human trials, because regulators and drug developers don't know how to build a safe and informative trial given the novelty of the methods that produced the candidate. So you essentially end up redoing a lot of the work that the AI allowed you to skip in order to do the work that remains. Perhaps a future innovation in "explainable" AI could help here, but that such a technology would itself need to be clinically validated in order to solve this problem.

Source: spent 9 years at a AI drug discovery startup. We advanced 8 clinical candidates and every one of them ran into some variant of this issue.