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The fundamental problem with AI (here meaning, neural-network based applications) applied to biology is that pattern recognition doesn't get you very far. When

The fundamental problem with AI (here meaning, neural-network based applications) applied to biology is that pattern recognition doesn't get you very far. When designing a small molecule drug, for example, the goal is to find a molecule that will slot into some protein (or other biomolecule) of interest in just the right way; whether it does so is dependent on shape in complex and impossible-to-predict ways. As an analogy, take the task of making a key (drug) to open the front door of an apartment across town (disease-relevant target); your "training data" (known drug-y molecules) are the keys for a bunch of other apartments sampled mostly randomly from around town. Obviously, you can make any number of plausible key-oid objects that would pass visual inspection as potential keys to the target, but even a godly-intelligent post-Bostromian superAI couldn't solve the problem in any principled way. You just need information about the actual lock you want to open, plain and simple. There are many, many, many other open problems in biology, of course, ranging all along the conceptual gamut, so I can't say there's nothing where AI would be a killer app. And there's also the sort of last-ditch null argument that, well, being smart helps humans do biology, so computers that are smart must be useful somehow. But typical progress in this field is from patiently acquiring new data. There's no point where you know "enough" of the picture to infer the rest with any degree of certainty- the degrees of freedom in the "design" of biological systems is simply too huge.

steve_musk 9y ago
I’m not sure this is a great analogy. If we didn’t know anything about the locks (target binding sites) I would agree with you, but there is no reason to enforce that restriction. A pipeline that given a target binding could predict a number of potential drug structures with high accuracy would be extremely useful. Going with your analogy, this would be like training a net using a database of lock structures and their corresponding keys, and then having it predict a key for a given lock. That seems fairly doable for current ML techniques. Obviously actual drug design is more complicated than this, and you have to consider side effects. But I don’t think it’s as hopeless as your analogy.
- maxander 9y ago
  > Going with your analogy, this would be like training a net using a database of lock structures and their corresponding keys, and then having it predict a key for a given lock. If we have that, then the problem is entirely trivial- just look at the lengths of the tumblers, and give the key teeth that match. And so it is, with the corresponding increase in complexity, in biology- given a complete and confident structure of the target, we have (non-AI, reasonably reliable) chemical modelling methods to see whether and how a given compound will bind; iterate molecules until you find one that works and you've found your "key" quite cheaply. So, there's another limitation to the practicality of AI- when it's not irreducibly complex and intrinsically unknown, biology is usually mechanistic and deterministic enough that conventional approaches can reason about it quite effectively without resorting to anything so fancy as a neural network. Biology is almost always more complicated than a given description, so yes, the real problem is more complicated. As you mention, the key we create must not only fit the target lock, but it must also not fit any other locks that might cause problems. Perhaps AI methods could try to make a working key that is un-key-like as possible, to make it unlikely to fit off-target locks; potentially useful. But, absent knowledge of the various perverse configurations that locks in the wider world can have, the confidence the AI method's inferences are very limited- roughly speaking, it's attempting to make a classifier for a population that is mostly unknown. We might hope for a severalfold increase in the number of successful drug candidates, in the best case, but not a fundamental disruption of the pharmaceutical research process.
  - steve_musk 9y ago
    > given a complete and confident structure of the target, we have (non-AI, reasonably reliable) chemical modelling methods to see whether and how a given compound will bind; iterate molecules until you find one that works and you've found your "key" quite cheaply. I am a researcher in the computational chemistry/biophysics field, and from what I can tell this is not the case (would love to be corrected if I am mistaken; my research is not directly related to drug design). There are various computational ways to to test whether a molecule will bind to a target site, but they are either too inaccurate or too computationally expensive to claim that this problem is "solved" by chemical modeling methods. Pharma companies are still wasting money sending drug structures that have been validated by computational methods to organic chemists to synthesize that end up not binding as expected.

steve_musk 9y ago

I’m not sure this is a great analogy. If we didn’t know anything about the locks (target binding sites) I would agree with you, but there is no reason to enforce that restriction. A pipeline that given a target binding could predict a number of potential drug structures with high accuracy would be extremely useful. Going with your analogy, this would be like training a net using a database of lock structures and their corresponding keys, and then having it predict a key for a given lock. That seems fairly doable for current ML techniques. Obviously actual drug design is more complicated than this, and you have to consider side effects. But I don’t think it’s as hopeless as your analogy.

maxander 9y ago
> Going with your analogy, this would be like training a net using a database of lock structures and their corresponding keys, and then having it predict a key for a given lock. If we have that, then the problem is entirely trivial- just look at the lengths of the tumblers, and give the key teeth that match. And so it is, with the corresponding increase in complexity, in biology- given a complete and confident structure of the target, we have (non-AI, reasonably reliable) chemical modelling methods to see whether and how a given compound will bind; iterate molecules until you find one that works and you've found your "key" quite cheaply. So, there's another limitation to the practicality of AI- when it's not irreducibly complex and intrinsically unknown, biology is usually mechanistic and deterministic enough that conventional approaches can reason about it quite effectively without resorting to anything so fancy as a neural network. Biology is almost always more complicated than a given description, so yes, the real problem is more complicated. As you mention, the key we create must not only fit the target lock, but it must also not fit any other locks that might cause problems. Perhaps AI methods could try to make a working key that is un-key-like as possible, to make it unlikely to fit off-target locks; potentially useful. But, absent knowledge of the various perverse configurations that locks in the wider world can have, the confidence the AI method's inferences are very limited- roughly speaking, it's attempting to make a classifier for a population that is mostly unknown. We might hope for a severalfold increase in the number of successful drug candidates, in the best case, but not a fundamental disruption of the pharmaceutical research process.
- steve_musk 9y ago
  > given a complete and confident structure of the target, we have (non-AI, reasonably reliable) chemical modelling methods to see whether and how a given compound will bind; iterate molecules until you find one that works and you've found your "key" quite cheaply. I am a researcher in the computational chemistry/biophysics field, and from what I can tell this is not the case (would love to be corrected if I am mistaken; my research is not directly related to drug design). There are various computational ways to to test whether a molecule will bind to a target site, but they are either too inaccurate or too computationally expensive to claim that this problem is "solved" by chemical modeling methods. Pharma companies are still wasting money sending drug structures that have been validated by computational methods to organic chemists to synthesize that end up not binding as expected.