Re:A watershed moment for protein structure prediction
by ovi256 on Monday January 20, 2020 @10:01AM
(#22099238)
>Won't some candidates simply be too complex and unstable to reliably produce using existing mammalian or baculovirus platforms?
You can add that to the objecting function that your model training function is optimizing, ensuring model output is not "too complex or unstable to reliably produce".
Re:A watershed moment for protein structure prediction
by RocketSyntax on Monday January 20, 2020 @09:12AM
(#22098879)
You can use permuted feature importance to tell you what inputs are most correlated with the condition. WAYYYY better than the binary correlation methods most statistics use for each feature.
Far from it. Prediction looks like a tool in the arsenal for better understanding. One still has to correlate the structure with the complex interactions in vivo. Even using AI in classification mode, where we can segment a large atlas of tumor cells and identify a dozen or so classes of cell anomalies may lead to faster breakthroughs in immunotherapy.
What I am trying to wrap my head around is the synthesis problem. Say AlphaFold generates a promising candidate. One that does not exist naturally. You still need the DNA or mRNA transcription sequence to synthesize the protein, right? Won't some candidates simply be too complex and unstable to reliably produce using existing mammalian or baculovirus platforms?