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that is a great point.

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

The primary missions in drug development are efficacy and safety. AI can help answer clear well-formed efficacy questions like, "Does this molecule fit a chosen target molecule"? But it can't help with bigger efficacies like, "Will hitting this target make a sufficient difference in managing this disease"? Or any safety questions like, "Does the molecule also hit any other molecules somewhere in the body (or population) that might screw up something else"? And unfortunately it's safety failures that eat up 90% of drug development costs (esp. in Phase III).

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.

Well, as far as I know the state-of-the-art results on current drug toxicity prediction benchmarks (e.g., Tox21) are held by deep neural networks; it seems like recent AI advances HAVE proven useful in that regard.

Do you have a source for that? I'm super interested into reading more.

I had to do Tox21 I was using Bayesian Network for drug toxicity at the FDA we used the DILI database.

This is the paper from the winners of the Tox21 Challenge: https://www.frontiersin.org/articles/10.3389/fenvs.2015.0008.... (fun fact: one of the authors is the inventor of the LSTM)

> 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?

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/).

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.

I'm not an expert in this space, but lead generation doesn't seem like a fundamentally bad place to do this - if you could accurately rank leads this would deal with the failed drugs - it just hasn't achieved the results people would like.

"~$800M on failed drugs for every ~$900M in revenue" Do you have a source for this? Looking at big Pharma the marketing budget is at least 2X the R&D budget so the numbers really look off from what you are posting?

Looking at Pfizer last q. for example revenue 53 bil. R&D 7.5 bil

Most of the actual R&D is done by smaller companies, and then the large companies (e.g. Pfizer) buy up the compounds in Phase ~2. They do have some of their own development, but it isn't the majority for most players. They'll primarily take drugs through Phase 3, then deal with synthesis, distribution, and marketing.

It is hard to look at a single company to see how the money is spent on failed drugs.