AI in drug discovery is overhyped: examples from AstraZeneca, Harvard, Stanford
The fundamental problem with AI (here meaning, neural-network based applications) applied to biology is that <i>pattern recognition</i> doesn&#x27;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 &quot;training data&quot; (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&#x27;t <i>solve</i> the problem in any principled way.  You just need information about the actual lock you want to open, plain and simple.<p>There are many, many, <i>many</i> other open problems in biology, of course, ranging all along the conceptual gamut, so I can&#x27;t say there&#x27;s <i>nothing</i> where AI would be a killer app.  And there&#x27;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 <i>somehow</i>.  But typical progress in this field is from patiently acquiring new data.  There&#x27;s no point where you know &quot;enough&quot; of the picture to infer the rest with any degree of certainty- the degrees of freedom in the &quot;design&quot; of biological systems is simply too huge.