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.
Needless to say that the application of AI for various pharmaceutical research tasks has become a widely discussed topic at practically all industry conferences and symposia. Besides, there is a long list of industry events specifically focused on AI in life sciences -- and they are crowded with top leaders from “big pharma”.
The key reason AI has taken pharmaceutical industry by storm is obvious: technology giants like Google, Apple, Amazon, Facebook, Microsoft, managed to demonstrate striking practical feasibility of the technology in the areas of natural language processing, text, image and video processing etc (one obvious practical example is a cohort of personal assistants --- Alexa, Siri, or Google Assistant -- you can try one of them right away and see how it blows your mind with what it can do). There is no better proof of concept than a demonstrated, measureable, practical result.
A lot has been said about how transformative AI is, and I believe biopharmaceutical leaders have successfully passed a stage of “do we actually need AI in our organization?” and are starting to usher into the next stage: “how to practically adopt AI at scale?”.
Tactics vs Strategy
While outsourcing AI-driven research from specialized vendors in a form of joint projects and research collaborations will remain a suitable mode of action, as a relatively simple and almost risk-free solution to try new things -- with little initial investment on the side of pharma most of the commitment lies on the shoulders of outsourcing partners -- it may only serve as a short-term tactical maneuvering.
Strategically-wise, pharmaceutical companies will inevitably have to focus (some already focused) on trying to build internal AI-powered research workflows and business processes and, ultimately, create own core know-how in what relates to applying AI to in-house research datasets.
I presume, as was with the digitalization of pharmaceutical industry over the last half a century (proliferation of personal computers and Internet, corporate digital systems (ERPs, CRMs, ELNs etc, cheminformatics and bioinformatics tools, e-commerce, etc), it is democratization of AI-technology and its components, that will be a key driver in the upcoming “AI-zation” of pharmaceutical research.
Making AI accessible to (bio)pharmaceutical researchers
In the context of technological progress, democratization is defined as making new tech accessible to the wider community of professionals, beyond just tech domain experts. The process of technology democratization is driven by the standardization of parts and modules, specialized tools, architectures, common platforms, user interfaces, designs, processes etc. It involves creating industrial standards for training personnel and crystallizing best use practises across various types of professionals.
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