AlphaFold: Using AI for scientific discovery
The blog is light on details but it appears <i>Alpha</i> is being used for branding here. AlphaFold seems to depart in a significant way from the perfect information game playing agent's architecture. A few questions spring to mind:<p>1) What is the architecture of the generative network and where exactly does it fit in the pipeline?<p>2) What is the interaction with the database? Is there an encoder being trained with real sequences further augmented with variations using the generative network?<p>3) What is the structure of the neural network that encodes the sequence? Is it a graph network, LSTM or simple conv-net?<p>4) The gradient descent step is very vague. Is it a physically based differentiable model (not a neural network) whose parameters are being optimized with gradient descent using automatic differentiation? Or something else? In short, there's some detail on scoring but how are the proposals being generated?<p>Questions aside, the results speak for themselves and are head and shoulders above all other showings. I wonder what it feels like for someone whose been in the field for years.<p>Despite the high score, there's still a long way to go before results reach real world utility. It's also worth keeping in mind that from a systems biology perspective, protein folding is only a small part of what makes getting clinically useful results difficult.<p>I might have missed something but I could not find anywhere indications of an intention to publish further details. That would be disappointing if such were indeed the case.