China has been becoming a research powerhouse in many fields of science, but it still is a minor ...
The first biologics drug, humanized insulin (5.8 kDa), became available in 1982 following the advent of biotechnology, and ...
Recently D.G.Brown and J.Boström of AstraZeneca published an insightful analysis, where they reviewed lead generation research strategies behind ...
(Edited version of this post originally appeared in Forbes) “It is not the strongest of the species that ...
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How Pharmaceutical And Biotech Companies Go About Applying Artificial Intelligence in R&D
China has been becoming a research powerhouse in many fields of science, but it still is a minor player when it comes to pharmaceuticals. However, recent developments suggest that China’s role in pharmaceutical research may change in the near future.
Choosing the right biological target or a combination of targets is a fundamental task for any successful drug discovery project. All the subsequent efforts -- be it a small molecule hit identification, lead optimization, pharmacokinetic studies, or a clinical trial -- will just be as effective, at the end of the day, as was the initial decision to choose one target or another.
Recently D.G.Brown and J.Boström of AstraZeneca published an insightful analysis, where they reviewed lead generation research strategies behind 66 small molecule clinical candidates published over 2016-2017 in Journal of Medicinal Chemistry.
Below is a brief summary of some key statistics and ideas outlined in the work (I encourage reading the original paper, it contains a ton of valuable insights and a strong list of references).
Interviews with experts
The previous year was rich in discussions and events one way or another related to potential applications of artificial intelligence (AI) advances for the benefit of drug discovery and development.
(Note: For the sake of simplicity, the term “AI” will be applied herein interchangeably with terms like “machine learning” (ML), “deep learning” (DL), “neural networks” (NN) etc., although conceptually, those terms are quite different in meaning. The term AI describes a field of computer science studying how to make a computer intelligent at doing something, while terms like ”machine learning”, “deep learning”, and “neural networks” relate to algorithms and methods by which it can be achieved.).
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!
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