If you work in Life Sciences & Health, you’re likely aware of what Data Science and AI can bring to the table when it comes to sparking innovation. However, if you are not Big Pharma, it’s the how that slows down most LSH companies and organizations. We believe that Elsevier’s road-tested data and semantic technologies can address the challenges of Life Science data.
Why is everyone talking about Digital Transformation?
Admittedly, digital transformation has already become a rather vague catch-all catchphrase. But at its root, it’s about using data and technology to derive business benefit, whether it is speedier innovation, amplified risk management or greater efficiency. This path usually involves applying AI to this data.
AI is clearly no longer “up-and-coming”. For example, the use of AI by U.S. businesses has risen by a third between 2018 and 2020. It’s become a key competitive differentiator, backed by a clear causal link: greater digital maturity means greater financial performance.
“When I first started in this role, outside of Silicon
Valley it was a real challenge to deliver AI- and machine-learning-powered
innovation at scale,” says Mark Sheehan, Elsevier’s VP of Data Science for the
Life Sciences. “And so, any tangible benefits were limited. But now there are a
number of technologies to build on to drive the state-of-the-art forward. We’ve
worked really hard at Elsevier in recent years to leverage these technologies
and ‘pave our roads’ for easy and fast AI development and deployment.”
In other words, AI’s time has truly come.
Search, discover, predict
For pharmaceutical companies undertaking the long and expensive road to bringing a new drug to market, AI can make all the difference in terms of saving time and money. For example, it can search for the latest related research. It can discover connections between chemical structures and specific biological activities like the Biology Knowledge Graph, which provides deep evidence for disease biology related decisions. It can even work to predict optimal reaction conditions so you know what to do – or not to do – next.
“The data science teams in Elsevier are not only bringing our wealth of high-quality historical data and scientific expertise to the table,” says Mark. “but we’re also using cutting-edge AI and machine learning technologies to power our own products and services, such as with our chemical database Reaxys and our biomedical research database Embase. Meanwhile, we’re also working with a whole range of partners to build an impressive research pipeline for AI-driven innovation in the Life Sciences.”
Turning data into insights: in search of smarter
pipelines
The sense of urgency brought on by COVID-19 exposed a clear
trend: data-driven organizations tend to be more resilient and better able to achieve
better and faster results – in terms of steering change, maintaining revenue
and pushing through innovation. In other words, they are able to transform
their data into relevant and actionable insights that create value.
Many felt left behind. Some began to wonder how they themselves
could accelerate – or even start – their own process of digital transformation.
“Elsevier had already started well down this road of
greater automation and leveraging of AI prior to the pandemic,” notes Mark. “And
because we already worked closely together across distributed agile teams
internally and with our partners, we were even able to accelerate our
adoption of new technologies and pace of innovation – despite the challenge of multiple
lockdowns and not being able to meet face-to-face.”
Research, develop, predict
Elsevier has a wide range of road-tested and respected R&D solutions in place for those who are not necessarily data scientists but still want to leverage the benefits of AI.
For instance, for best-in-class retrosynthesis, the previously mentioned Reaxys was recently expanded to better aid in the development of new compounds and molecules – while keeping you up-to-date on the latest patents. Released during the pandemic, Entellect’s Reaction Workbench supports predictive reaction capabilities and even allows you to create your own algorithms.
The tip of the innovation iceberg
“Our customers need to innovate faster, and therefore
need to use machine enrichment and prediction to aid and accelerate their
decision-making and prioritizing for research,” says Mark. “As a result, the pace
and scale of how we employed technologies in Elsevier Life Sciences also
required a gear change.”
For example, five years ago, human curators at
Elsevier closely excerpted articles from around 400 journal titles a year for
Reaxys, but now with machine support the number has risen to 16,000 journals.
In addition, Elsevier has driven an even bigger growth in patents coverage with
20 million patents processed so far in 2021 alone.
“Certainly, many of our customers are surprised by
what we already offer. But I’m confident they’ll be even more impressed by what
we have coming up in our development pipeline for the next rounds of innovation.
The journey has only just begun!”
