Seeing through the hype of deep learning for drug discovery

The frequency seems to be increasing where I see something that sets me off (head in hands saying why, why why?)..Over the past few years I have seen AI/ Deep Learning and drug discovery appearing often and that may not be a good thing. Now this could be the PR engines of the VC’s, companies involved or pharma…or Nvidia needing something new to talk about (or all of these).. Today it was the Wall Street Journal hyping AI up, last year it was the Economist.

Now I am as bullish as the next person on the power of these technologies to help with drug discovery but – they really have not been thoroughly tested. Last year I published a small review in Pharmaceutical Research which clearly highlighted that All of the papers using Deep Learning for drug discovery etc. had not prospectively tested the approach and the level of comparison with other methods or even assessment of different descriptors was not performed. My bottom line conclusion was that much more needed to be done to show that Deep Learning really was worth the effort for pharma before we spend time and money.  So we are digging into it here as well. We invested in a small server and we have a team doing the experiments that others seem to have forgotten are needed before we can say if its worth throwing more at it. We have to look at technology like this objectively. Is one algorithm really better than all the other algorithms we have? Could it be a competitive advantage for pharma vs what not doing Deep learning? AI / Deep learning is really not new but it is getting attention. Some of it is needed but I urge caution. We have been here before in pharma having jumped on every new technology that has come along as the savior, and after a few years it was clearly not to be. AI / Deep Learning is that new thing..

Today’s article had a quote by John Baldoni (GSK SVP of platform technology and science) saying “the aim is to use AI to cut development time down to a single year from more than 10 in some cases”. I think while this would be great in theory it puts an enormous pressure on the techniques to deliver. Frankly such a quote is not helpful and just sows more doubt and add fuel to the over-hyped machine. Big Pharma is not learning from the lessons of the past.

“One swallow does not make a summer,
neither does one fine day;
similarly one day or brief time of happiness does not make a person entirely happy.”



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