AI in drug discovery is overhyped: examples from AstraZeneca, Harvard, Stanford
1) As someone who does NLP and AI for Evidence Based medicine [1] I can only agree. I build natural language processing tools, together with the team, for EBM. I have /no/ idea what to do next. Please. Help me.<p>2) Rant: here's the thing, we have SVMs, LSTM, BiLSTM-CRF, Knowledge Graphs, 28M articles, deploy infrastructure, tests, you know all the cool stuff. We can't sell it. It's PubMed this, PubMed that. There's no breaking that spell. So instead of building a better search engine, we focused on better analytics. Is it cool, sure. Is it as good as all the KoL stuff from LexusNexis, nope. Medicine is /hard/, if you think your app is hard: medicine is /harder/. Am I trying to cover my ass, sure. But seriously, deal with everything being non-standard and proprietary for long enough and you'll feel the same way. 80% of my job is saying "no" to people who aren't going to pay in the first place (Sanofi, Novartis, etc). They don't care. I'm just going to ride this out, throw some blockchain in somewhere where it makes absolutely 0% sense, call it a day. Seriously, NLP/AI for medicine: it's crap 100%.
And, I'm a "senior developer" with publications behind my ass on this. I don't know what to do instead of riding the hype train. Seriously: anyone out there that knows what to do with 100M+ medical publications in a 100M+ knowledge graph, be my guest: I'd hire you in a heart beat. But it's hard and there is nothing to be gained AFAIK.<p>[1]: <a href="http://growthevidence.com/joel-kuiper/" rel="nofollow">http://growthevidence.com/joel-kuiper/</a>