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Over the past two to three years, the pace of digital transformation has accelerated with improved performance, power and adaptability of tools and investments in cloud computing, data architecture and visualization technologies. There are also a growing number of use cases for machine learning and, in the future, quantum computing, which will accelerate the development of molecules and formulations.
The vast digital transformation underway in R&D enables researchers to automate time-consuming manual processes and open up new avenues of research into thorny issues that have failed to achieve breakthroughs. This new report, based on interviews with R&D leaders at companies such as Novartis, Roche, Merck, Syngenta and BASF, explores use cases, best practices and roadmaps for the digitization of science .
Explore models in complex data sets
Rich, accessible and shareable data is the fuel on which today’s revolutionary analysis and calculation tools are built. To ensure that datasets are usable for scientific purposes, large companies focus on FAIR data principles (findable, accessible, interoperable and reusable), develop robust metadata and governance protocols, and use tools advanced data analysis and visualization.
The digital transformation is opening up R&D horizons in fields such as genomics that could lead to breakthroughs in precision medicine. It also creates opportunities for decentralized clinical trials, unleashing future innovations in digital products and wearable healthcare devices.
Get to the right study faster
Experiments and clinical trials come at a huge cost to both industries, both financially and in terms of human and scientific resources. Advanced simulation, modeling, AI-based analysis, and quantum computing help identify the strongest candidate for new therapies, materials or products, allowing only the most promising to move on to the expensive experimental phase.
Organizational overhaul
R&D leaders encourage bottom-up innovation by giving research teams the freedom to experiment with new technologies and techniques. They also drive top-down strategic initiatives to share ideas, align systems, and channel digital transformation budgets. As in any industry, artificial intelligence and automation are changing the way we work in scientific research. Rather than being seen as a threat to research careers, large organizations in the pharmaceutical and chemical industry are demonstrating that digital offers new opportunities for collaboration and removal of silos. They celebrate victories, encourage feedback, and maintain open discussions about culture change in the workplace.
Download the full report.
This content was produced by Insights, the personalized content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.
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