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Assessing Productivity of Pharmaceutical AI: Any Results Beyond Hype?

   by Andrii Buvailo    910
Assessing Productivity of Pharmaceutical AI: Any Results Beyond Hype?

A background context -- opportunities and challenges

Current widespread interest towards artificial intelligence (AI) and its numerous research and commercial successes was largely catalyzed by several landmark breakthroughs in 2012, when researchers at the University of Toronto achieved unprecedented improvement in the image classification challenge ImageNet, using their deep neural network “AlexNet” running on graphics processing units (GPUs), and when that same year Google’s deep neural network managed to identify a cat from millions of unlabeled Youtube videos, representing a conceptual step in unsupervised machine learning.

Will 2019 Bring Positive Signals for the Declining Antibiotics R&D Market?

   by David Shlaes    492
Will 2019 Bring Positive Signals for the Declining Antibiotics R&D Market?

Antibiotic R&D has had a particularly bad year starting with The Medicines Company who abandoned their antibiotic R&D efforts and sold their antibiotic assets to Melinta late last year right after getting approval for vabomere. This year both Sanofi and Novartis abandoned their antibiotic R&D efforts and divested their clinical and preclinical assets. Allergan, holder of the North American rights to ceftaroline, dalbavancin and ceftazidime-avibactam, also announced that they would divest their antibiotic assets. I have not heard that they were successful. Achaogen has now undergone two efforts at “restructuring” involving virtually eliminating all R&D and has essentially put up the “for sale” sign just after achieving approval for plazomicin. Finally, Melinta abandoned their antibiotic R&D efforts in the face of miserable sales of their recently launched antibiotics including delafloxacin and vabomere.

Democratizing Artificial Intelligence For Pharmaceutical Research

   by Andrii Buvailo    845
Democratizing Artificial Intelligence For Pharmaceutical Research

Over the last five years the interest of pharmaceutical professionals towards machine learning (ML) and artificial intelligence (AI) has measurably increased -- while only one “AI-related” research collaboration involving “big pharma” appeared in the news in 2013, the number of such events increased up to 21 in 2017 alone, involving some of the top pharma players like GSK, Sanofi, Abbvie, Genentech, etc.

Top AI in Pharma and Healthcare Conferences in 2019 You Can’t Miss

   by Andrii Buvailo    5555
Top AI in Pharma and Healthcare Conferences in 2019 You Can’t Miss

Machine learning and artificial intelligence have become widely discussed topics in the area of life sciences and healthcare over the last several years. While a lot of pharmaceutical companies and healthcare organizations express considerable interest in possible new opportunities, associated with the use of artificial intelligence for early drug discovery, clinical trials optimization, and business intelligence, a considerable gap still exists when it comes to understanding new technologies by pharmaceutical professionals and leaders. The key questions here are these:

  • What machine learning / AI can and can’t do for pharmaceutical industry

  • What should be done to harness practical and measurable value out of machine learning / AI?

  • How it should be done and what are the timelines for getting returns on investments? 

How Big Pharma Adopts AI To Boost Drug Discovery

   by Andrii Buvailo    20917

(Last updated 08.10.2018)

The type of artificial intelligence (AI) which scares some of the greatest minds, like Elon Musk and Stephen Hawking, is called “general artificial intelligence” -- the one which can “think” pretty much like humans do, and which can quickly evolve into a dangerous “superintelligence”. There is a notion that it might be invented in the nearest decades, but today we are definitely not there yet. The AI which is making headlines these days is a “narrow artificial intelligence”, a limited type of machine “intelligence” able to solve only a specific task or a group of tasks. It can’t go anywhere beyond specifics of the problem for which it is designed, so apparently, it will not hurt anyone in the nearest time. But already now it can provide meaningful practical results on those narrow tasks, like natural language processing, image recognition, controlling self-driving cars, and helping develop new drugs more efficiently. With the ability to find hidden and unintuitive patterns in vast amounts of data in ways that no human can do, AI represents a considerable promise to transform many industries, including pharma and biotech.