<story><title>AlphaFold: Using AI for scientific discovery</title><url>https://deepmind.com/blog/alphafold/</url></story><parent_chain><item><author>Cybiote</author><text>The blog is light on details but it appears &lt;i&gt;Alpha&lt;/i&gt; is being used for branding here. AlphaFold seems to depart in a significant way from the perfect information game playing agent&amp;#x27;s architecture. A few questions spring to mind:&lt;p&gt;1) What is the architecture of the generative network and where exactly does it fit in the pipeline?&lt;p&gt;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?&lt;p&gt;3) What is the structure of the neural network that encodes the sequence? Is it a graph network, LSTM or simple conv-net?&lt;p&gt;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&amp;#x27;s some detail on scoring but how are the proposals being generated?&lt;p&gt;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.&lt;p&gt;Despite the high score, there&amp;#x27;s still a long way to go before results reach real world utility. It&amp;#x27;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.&lt;p&gt;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.</text></item></parent_chain><comment><author>hmartiniano</author><text>I&amp;#x27;m not really &amp;quot;in the field&amp;quot; but I did some computational work on protein folding in the past.&lt;p&gt;I have a hunch on how they are doing this.&lt;p&gt;From the blog post it appears that the network is using angles and distances of aminoacids in a given sequence in know structures to predict good starting point(s) for regular molecular dynamics-based structural optimization (what is called the &amp;quot;gradient descent step&amp;quot; in the post).&lt;p&gt;If I&amp;#x27;m wrong then I&amp;#x27;ll just have to try this approach myself one day...</text></comment>
<story><title>AlphaFold: Using AI for scientific discovery</title><url>https://deepmind.com/blog/alphafold/</url></story><parent_chain><item><author>Cybiote</author><text>The blog is light on details but it appears &lt;i&gt;Alpha&lt;/i&gt; is being used for branding here. AlphaFold seems to depart in a significant way from the perfect information game playing agent&amp;#x27;s architecture. A few questions spring to mind:&lt;p&gt;1) What is the architecture of the generative network and where exactly does it fit in the pipeline?&lt;p&gt;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?&lt;p&gt;3) What is the structure of the neural network that encodes the sequence? Is it a graph network, LSTM or simple conv-net?&lt;p&gt;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&amp;#x27;s some detail on scoring but how are the proposals being generated?&lt;p&gt;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.&lt;p&gt;Despite the high score, there&amp;#x27;s still a long way to go before results reach real world utility. It&amp;#x27;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.&lt;p&gt;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.</text></item></parent_chain><comment><author>nopinsight</author><text>It looks like some details might be published in the CASP13 conference.&lt;p&gt;&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.predictioncenter.org&amp;#x2F;casp13&amp;#x2F;index.cgi&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.predictioncenter.org&amp;#x2F;casp13&amp;#x2F;index.cgi&lt;/a&gt;&lt;p&gt;For such a breakthrough, there is a good chance they are writing another article for Nature or Science, even aiming for another cover feature.</text></comment>