AI has redefined the drug discovery and development process.
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 longstanding 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.
Another great example is with the infrastructure side of big pharma. The entire pharmaceutical lifecycle, i.e., going from development to manufacturing and ultimately into consumer hands, requires deep investments in supply chain optimization, tailored transportation and logistics, cold chain management and many other complex infrastructure pieces. AI has found a home here as well. Take for example SkyCell, a pharmaceutical cold chain logistics pioneer, which just inked a deal with Microsoft to integrate SkyMind, an advanced supply chain solution in conjunction with Azure cloud. With SkyMind, the company will now be able to “seamlessly access real-time shipment data, predictive insights, and automated alerts within their existing Microsoft ecosystem, enhancing efficiency, compliance, and decision-making.”
Millions of companies have found incredible promise with integration into cloud services. For one, it has allowed small companies to rapidly scale their offerings and products without physical server and space limitations. Additionally, the large cloud service providers today have pioneered many of their AI capabilities through their cloud suites, meaning that this is the easiest way customers can access and take advantage of these tools. Pharmaceutical companies moving into the cloud, as witnessed with the example above, poses a significant advantage given just how much data and information is handled in the entire drug development life-cycle.
While it is certainly still early days for the marriage of pharma and AI, the value proposition is inevitably starting to emerge more blatantly. Companies making bold investments in this technology will undoubtedly face growing pains as they figure out how to best use it for their specific needs; however, the hope is that the value they eventually harness will ultimately lead to better outcomes and products for consumers.
