for those not familiar with where the money really goes in R&D, and where the biggest opportunities for improvement are, check out these charts [1] and [2] from the seminal paper on calculating the cost of getting a drug approved [3].
the biggest areas to improve R&D productivity lie in 1) picking more validated targets to reduce Phase 2 and 3 failure rate and 2) reducing the cost of lead optimization (basically the process of turning a compound that has the desired impact on the target (target is a molecule implicated in disease that you want to effect with a drug) into a molecule with drug like properties (ie gets where you need it in the body, is safe, can be manufactured and delivered efficiently))
im not an AI person, but could AI play a role here? there are plenty of validated targets that are "undruggable"; could AI help find as-yet-undiscovered molecules that could engage these targets? or could AI somehow make the med chem / lead optimization process easier?
[1] https://media.nature.com/m685/nature-assets/nrd/journal/v9/n... [2] https://media.nature.com/m685/nature-assets/nrd/journal/v9/n... [3] https://www.nature.com/articles/nrd3078
Until Wall St (or Sand Hill Rd) understands that domain-agnostic low-info approaches like AI are incapable of answering complex questions that require teams of PhDs steeped in decades of doing both chemistry and biology, the notion of CADD will continue to miss the mark and waste megabucks.
I had to do Tox21 I was using Bayesian Network for drug toxicity at the FDA we used the DILI database.
Can you talk more about what you've been thinking about here?
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.
It is hard to look at a single company to see how the money is spent on failed drugs.
Anyway, the primary reason that AI for drug discover is overhyped is that the sort of problems AI is good at solving don't line up well with the unsolved problems in the drug discovery pipeline.
This article, for example, focuses a lot on lead generation. Lead generation is the easiest aspect of the problem to tackle using AI, and so most people doing research start out trying to build a foundation in this space. However, it doesn't actually represent the majority of the cost.
Drug makers typically spend about ~$800M on failed drugs for every ~$900M in revenue. They aren't spending that $800M on leads, finding leads is fairly easy. They are spending that money on drugs that fail in Phase 2 and Phase 3, which is more about off-target side effects, bulk formulation and synthesis, patient population differences, drug-drug interactions, etc.
It would be nice having better leads, but there aren't a shortage of them that look good in vitro or even in vivo. It isn't until much later in the pipeline that the costs really add up, and failures there are expensive. 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.
The work this article talks about is more foundational, which is necessary but should not really be taken as anything more.