“It is not the strongest of the species that survives, nor the most intelligent, but the one most adaptable to change” — Leon C. Megginson
Recently, pharmaceutical companies have been demonstrating a vivid interest in artificial intelligence (AI) applications for various research needs, catalyzed by the illustrative practical achievements of AI in solving more “traditional” tasks — overcoming humans in chess and Go, recognizing speech and text, identifying faces in Facebook photos, driving cars and so on. The history of AI development was filled with ups and downs fueling a lot of skepticism among the life science community, mainly due to a lack of expertise in AI-related technologies.
Let’s see why the pharmaceutical industry is now in need of innovation more than ever, how it can navigate through the hype around AI and harness value and how long that might take.
Answering The “Why” Question
Recently, I came across an economic analysis of why the pharma industry is in a state of crisis — and even more than that — on the brink of a terminal decline. Omitting the mathematics behind the scenes, it boils down to a quite natural effect: The more the industry improves the standard of care, the more difficult and costly it becomes to improve any further. As a result, pharma organizations end up putting more and more efforts toward innovation (R&D) to just get diminishing incremental benefits and added value for patients, which results in diminishing overall return on investments. In other words, the low-hanging and most profitable fruits are always taken first, and then it becomes harder and harder to keep pace with improvement.
At the same time, R&D budgets at biopharma companies are usually proportional to revenues. When revenues contract, less money becomes available for investing back into R&D, leading to even smaller chances of improvement in the future. It is a vicious circle — pharma’s allegedly broken business model.
A way out of this situation is to adopt more efficient and automated processes, more data-driven decisions and smarter predictive analytics tools to increase R&D success. This is what AI-driven industry transformation is all about.
Answering The “How” Question
Adopting AI innovations is difficult mainly due to the lack of relevant expertise and understanding among life science professionals. There are several strategies to go about adoption:
More and more pharma companies now reach out to AI-driven drug discovery startups to access expertise and tools to create promising drug candidates based on existing hypotheses and experience. In a recent article, I reviewed some of the key examples of “pharma-AI” collaborations of this kind.
Developing Internal AI-Capabilities
A harder way to go about AI adoption for drug discovery is to develop internal expertise and infrastructure for applying AI tools at scale.
Importantly, some public cloud service providers are increasingly improving AI and machine learning (ML) capabilities available as a service, thereby eliminating the need for deploying and implementing the entire AI stack from scratch. Such services are available at different levels of abstraction, enabling pharma companies to use them to underpin internal expertise with ready-to-use machine learning capabilities.
Companies such as Google and Intel are well known for effectively maintaining intrapreneurship culture, which allows them to constantly innovate and launch new products. Did you know that Gmail was “born” this way?
Essentially, it is all about providing employees with some freedom and granting them some internal resources to realize initiative projects, preferably without too many bureaucratic hurdles. This approach seems to be a viable strategy of adopting AI innovations for internal drug discovery and development use cases.
Academic research is a driving force of AI innovations. I think industry-academia partnerships to identify new biological targets or promising new lead compounds will grow even further as pharma companies continue to explore AI’s capabilities.
Open Science Projects
Organizing open science projects and open R&D challenges is a valuable tactic to go about AI adoption (and evaluation) for drug discovery needs in baby steps — without too many financial risks involved. One such example is QuickFire Challenges by Johnson & Johnson Innovation.
Whatever model is preferred for AI adoption by pharmaceutical organizations, it should remain clear that integration of AI into drug discovery processes is not an information technology (IT) problem as much as it is an R&D problem, requiring real R&D budgets and strong interdisciplinary research teams. Just throwing some additional bucks at IT departments is not enough.
Finally, Answering The “When” Question
Concept validation in pharmaceutical R&D is slow compared to “pure play” applications. Indeed, when Facebook tags your photo using AI, you can check immediately whether the result is correct or not, allowing the system to learn on the go. When AI suggests a new molecule as a potential drug candidate, it might take months or years to prove whether it’s efficient in a lab and in clinical trials. There is a complex and lengthy learning loop. It slows down the progress in applying AI for these kinds of tasks.
Moreover, there is an “innovation versus implementation” gap which was nicely described in a recent Forbes post by David Shaywitz. A lot of us think of innovation in technology as revelations which immediately transform the ways we think and act — which is wrong. While some startling AI breakthroughs might be popping up in various labs on a monthly basis, it might take years for the industry to follow.
AI can certainly be regarded as the next big thing in the pharmaceutical industry, and those who will be more flexible to adopt new processes faster will gain a strategic advantage. However, the current drug discovery paradigm will not be momentarily “disrupted” by AI innovations. Rather, the transformation will gradually occur over a decade or so. This is something that investors will have to take into account.
By Andrii Buvailo
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