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Topic: ‘Machine Learning’


A Clear Example of AI Value For Drug Discovery Has Arrived

   by Andrii Buvailo    300
A Clear Example of AI Value For Drug Discovery Has Arrived

With all the hot discussions (for instance, here, here, here and here) going on right now among medicinal chemists, pharmaceutical researchers, and data scientists as to what artificial intelligence (AI) means for the future of drug discovery, the life science world has divided into “AI-believers”, “AI-atheists”, and “AI-agnostics”.

It is useless to repeat what has been many times said about successes of AI in areas like natural language processing, image processing, pattern recognition and self-driving cars (here is the summary), but few of us knew if those sort of results (or any meaningful results at all) could possibly be achieved with such complex systems as biological organisms… Finally, however, a hint of hope arrived.   

Outsourcing AI For Drug Discovery: Independent Expertise Is Key To Avoid Overhyped Claims

   by Mostapha Benhenda    1681
Outsourcing AI For Drug Discovery: Independent Expertise Is Key To Avoid Overhyped Claims

Investments in artificial intelligence (AI) for drug discovery are surging. Big Pharmas are throwing big bucks at AI. Sanofi signed a 300 Million dollars deal with the Scottish AI startup Exscentia, and GSK did the same for 42 Million dollars. Also, the Silicon Valley VC firm Andreessen Horowitz launched a new 450 Million dollars bio investment fund, with one focus area in applications of AI to drug discovery.

In this craze, lots of pharma and biotech decision-makers wonder whether they should jump on the bandwagon, or wait and see.

[Interview] The Current Status Of AI In Drug Discovery And Looking Forward Into 2018

   by Andrii Buvailo    961
[Interview] The Current Status Of AI In Drug Discovery And Looking Forward Into 2018

Without a doubt, the area of artificial intelligence (AI) has been a sensation lately -- judging by the amount of hype around this topic. But the hype is not a guarantee of a real breakthrough, which is defined by facts and measurable achievements, not just loud statements.

The fact is, however, AI-driven systems managed to learn chess at a champion level in just 4 hours, and beat human champions in Jeopardy, and Go -- we all know that. And Facebook can recognize faces in a blurry photo where you are hanging out with a bunch of friends -- not worse than you (human) can do. You can try and see it for yourself anytime -- it works!

If it is not enough, here is a list of pretty much everything AI can do already today, speaking in practical terms. With this, it is becoming obvious that the progress in the AI space is quite real and the practical benefits are quite tangible, albeit there are a lot of technological and organizational challenges to overcome yet.

What is important to realize, though, is that the AI technologies hold a substantial disruptive potential, which can (possibly) transform the whole industries and redefine status quo. It is something to keep in mind if we talk about maintaining a long-term innovative competitiveness.