However, despite some of the claims being made, we are not at the point where this AI tool can be used for drug discovery. So this will help the science of structure determination go a bit faster in some cases.

For example, as already reported, it can generate folded structure predictions that can then be used to solve experimental structures by crystallography (and probably other techniques). Impressive predictions – but how do you know they’re correct?Īlphafold 2 will certainly help to advance biology. It is also difficult to see right now, despite DeepMind’s impressive performance, that this will immediately transform biology. This is impressive but you have to bear in mind that they didn’t know exactly which two-thirds of their predictions were closest to correct until the comparison with experimental solutions was made.* Would you buy a satnav that was only 67% accurate? For one thing, only two-thirds of DeepMind’s solutions were comparable to the experimentally determined structure of the protein. But we are not yet at the point where we can say that protein folding is ‘solved’. Of all the teams competing against one another they are so far ahead of the pack that the other computational modellers may be thinking about giving up. Should the experimentalist now all quit the lab and leave the field to Deep Mind?įirstly, there is no doubt that DeepMind have made a big step forward. Previously the laborious experimental work of solving protein structures was the domain of protein crystallographers, NMR spectroscopists and cryo-electron microscopists, who worked for months and sometimes years to work out each new structure. There followed much breathless reporting in the media that AI can now be used to accurately predict the structures of proteins – the molecular machinery of every living thing. Don’t believe everything you read in the media

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