[lowglow]

> If we could solve off-target side effects using AI, then we'd be in a whole different ballgame. Having banged my head against it for a while, I think it is possible, but will take a huge amount of investment.

Can you talk more about what you've been thinking about here?

[cing]

Not sure what OP is thinking, but you can look at this example of a commercial product designed for the prediction of off-target effects (https://cyclicarx.com/ligandexpress/).

[chris_va]

That route is doubly tough:

1) SAAS in the pharma world is mostly a waste of time. Culturally, they don't want to pay for anything except drugs. There is also a culture of sunk cost, where they do not want to prune drugs from their pipeline based on what some piece of software says.

2) This is a boil the ocean approach, which does not work statistically. There are 20,000 targets. Predicting bioavailability at each target is very difficult, and different populations have different expression patterns. Even if you have 99% precision/recall for each one, odds that you can help with selection enrichment are infinitesimal. Even if you restrict it to a handful of targets with strong known side effects, the state of the art predications are still not good enough to meaningfully improve the outcomes.

There are better approaches, nothing easy though.