Drug development is a critical component of every pharmaceutical company’s strategy, and Merck is investing in healthcare R&D and drug discovery to capitalise on this initiative. Walid Mehanna, chief data & AI officer, Merck Group, says artificial intelligence (AI) may help in many ways considering the volume of investments, the cycle times, and the risk of the investment. In a recent interview, he talks to Sudhir Chowdhary about the various data and digital solutions at Merck and tech adoption trends in the pharmaceuticals space. Excerpts:
Why are life sciences companies doubling down on digital for growth?
Considering multiple situations, it would not be ideal to identify AI and machine learning (ML) as niche technologies. This year has witnessed the growth of ChatGPT with generative AI, especially in the life science space, and gradually they are getting into the mainstream and are easily available and accessible. Many pharmaceutical companies are now doubling down on the automation opportunities they perceive for speeding up drug discovery and supply chain operations.
When it comes to AI and ML, the possibilities are endless. However, fundamental problems such as data quality and data governance must not be overlooked. All these aspects must be addressed first to lay the groundwork for AI and ML to make a difference in the overall organisation. In terms of paybacks, we have seen a few; and at Merck, we can conclude that the investments are mandated and smart investments.
Tell us more about your new project — Merck Data and AI Organisation.
Merck’s objective for the future century is to become a true leader in data and digital. Even though we are not the largest pharma company in the world, we are uniquely positioned because we are a 350-year-old science and technology organisation. Merck, with its distinct culture and organisational structure across its three business segments — healthcare, life sciences, and electronics — has a significant potential ahead of it. We ensure that we can leverage data and AI in an effective and efficient manner without having to reinvent the wheel for every single technology used.
How can organisations, especially those that have been around for decades, develop a successful data strategy?
There is no silver bullet, only hard effort that begins with a clear ambition. To leverage a proper data strategy, there should be a fine synergy between culture, mindset, and skillset. People, processes, and technology are the three aspects that we always use.Very importantly, data is not the responsibility of only IT; rather, data should be the responsibility of the entire enterprise.
How can AI play a key role in drug discovery?
Drug development is a critical component of every pharmacist’s strategy, and we are investing heavily in healthcare R&D and drug discovery to capitalise on this initiative. Cycle lengths are roughly seven years on average; therefore, AI may help in many ways when you consider the volume of investments, the cycle times, and the risk of the investment. For instance, in AIDDISON, our drug discovery software developed by our life science business, we are bringing our best AI techniques to market and making them available to our clients.
How is Merck using AI in its value chain?
We’ve been researching and experimenting with AI for quite some time. When it comes to incorporating generative AI into our value chain system, it is a relatively broad concept. However, we are looking to bring it in and apply it across the organisation – be it healthcare or R&D, drug discovery, operations and supply chain, or our electronic business.
AI is being used to forecast the maintenance and optimisation of our machinery. It helps in forecasting supply chain demands throughout the organisation, with a focus on sales. AI has the potential to tackle problems that are beyond the scope of existing technologies such as statistical techniques.
