If you open an industry publication, attend a conference, or dip into the spam filling your email inbox, you’ll know there is an excess of analysis on the priorities of pharma, biotech, and agrochemicals R&D. So while we weren’t expecting great surprises, we did gain interesting insights that are informing updates to our vision and strategy. Let me share some of those.
I’ll begin with perhaps the least surprising discovery; our customers’ overriding objective is enabling innovation, unlocking pathways to new products by identifying novel biological targets or new classes of active molecules. What was more interesting was the extent to which the importance of computational solutions in supporting this effort is no longer up for debate. Simulation is an established ‘must have’; the focus is on identifying the right tools to meet specific technical challenges (for example, in ligand-based design or virtual screening), on how to support collaboration across multi-disciplinary teams, and on finding solutions in which R&D teams can have confidence. This last point was linked to a drive for value. As computational solutions mature, research organizations seek partners that are proactive in helping them make good choices, deploying their budgets to best effect.
Artificial Intelligence which adds value
Artificial intelligence, of course, came up in every conversation; our customers approach AI with a blend of excitement, coupled with the scepticism of true scientists. They see its tremendous potential and all of them are already using AI methods. Indeed, many argue that they have been applying forms of machine learning for decades.
However, a key mantra was ‘Not AI for AI’s sake, but what can it do for me?’. They look out for AI ideas which add value, particularly when working alongside existing simulation methods and focused on key use cases. We heard that the combination of AI and physics-based methods is the most promising avenue for progress. We found an attitude to AI that reflects another key industry trend – an ever-strengthening focus on measurable outcomes in the planning and execution of R&D strategies.
Industry analysts speak of ‘digital transformation’ in R&D. Our customers tend to use more pragmatic language. They talk about becoming more data-driven and using insights from simulation as means to focus their wet chemistry. They identify the need to get medicinal and computational chemists better connected.
Data-driven insights to focus resources
Our customers focus on what works rather than on the hype, aware that, however you define ‘big data’, there is much to be gained from mining large, complex datasets – and this is one challenge where AI can excel. This is real digital transformation: always focused on accelerating the journey to the molecules that matter or, in the ideal scenario, the single molecule that matters. We call this digital molecular discovery.
Our customers make products that improve human health, support lifestyle choices, enable environmental protections, or open new possibilities for food production. They are creating a healthier world. Cresset aims to empower them by understanding in detail their digital molecular discovery needs and then meeting those needs, integrating the rigor of physics-based simulation with transformative AI. In future blogs, we’ll share more on how we’re realizing this vision.
If you like our vision please follow our story on LinkedIn as we work towards it. If you are not clear we have it right, we would welcome a opportunity to discuss our vision with you and hear more about the problems that you are facing.
