The last two years have witnessed explosive growth in the implementation of artificial intelligence for big healthcare organizations as well as large pharmaceutical companies. Initially, these investments were approached with pause, purely because the value proposition for AI use cases was still unclear. However, over time, as the technology stack has improved significantly and more emphasis has been placed on developing industry specific applications of AI, the value is slowly starting to emerge.

With regards to pharmaceutical companies, artificial intelligence has unlocked significant potential for organizations to leverage the best in technology to improve the drug discovery, research and development processes. For example, DeepMind, an innovative company under the Alphabet umbrella, has done significant work with AI and has created its AlphaFold ecosystem, which has redefined the way scientists can engage with computational biology and chemistry. Specifically, with the use of this model, researchers can essentially simulate and generate scenarios through which they can manipulate organic models and protein interactions to discover new enzymatic relationships and effects. This in turn has paved the way for new and quicker discoveries for drug development.

Even the more traditional and long standing players are embracing AI rapidly. For example, Eli Lilly recently struck a partnership with BigHat Biosciences to expand its AI driven drug development capabilities. Specifically, the partnership will leverage BigHat’s Milliner platform to address “multiple antibody attributes simultaneously – including affinity, specificity, immunogenicity, and manufacturability – with the goal of accelerating the development of biologics with improved therapeutic profiles.” While this work would have normally taken years otherwise, the platform’s machine learning capabilities are a significant boon for this process.