I don’t know, but if we were to reframe this as some software to take a hit from a GWAS, look up the small molecule inhibitor/activator for it, and then do some RNA-seq on it, I doubt it would gain any interest.
https://iovs.arvojournals.org/article.aspx?articleid=2788418
Ultimately we want effective treatments but the goal of the assistant isn't to perfectly predict solutions. Rather it's to reduce the overall cost and time to a solution through automation.
Genetic regulation can at best let us know _involvement_ of a gene, but nothing about why. Some examples of why a gene might be involved: it's a compensation mechanism (good!), it modulates the timing of the actual critical processes (discovery worthy but treatment path neutral), it is causative of a disease (treatment potential found) etc...
We don't need pipelines for faster scientific thinking ... especially if the result is experts will have to re-validate each finding. Most experts are anyway truly limited by access to models or access to materials. I certainly don't have a shortage of "good" ideas, and no machine will convince me they're wrong without doing the actual experiments. ;)
There's practically negative utility for detecting archeological sites in South America, for example: we already know about far more than we could hope to excavate. The ideas aren't the bottleneck.
There's always been an element of this in AI: RL is amazing if you have some way to get ground truth for your problem, and a giant headache if you don't. And so on. But I seem to have trouble convincing people that sometimes the digital is insufficient.