Do you know that biotech companies using AI-first approach have more than 150 small-molecule drugs in discovery and more than 15 already in clinical trials?
Building AI-based strategic intent requires approvals from stakeholders and for delivering on the promises requires action on strategic and operational milestones.
Over the past decade Artificial intelligence is making a strong wave towards drug discovery and is wonderful news for medicine & patient care. This AI-drug discovery pipeline is expanding at an annual rate of almost 40%.
Given the transformative potential of Artificial Intelligence, pharma companies need to plan for a future in which AI is routinely used in drug discovery processes.
This means spending timeto understand the impact of AI on R&D, which includes separating hype from actual achievement and recognizing the difference between individual digital solutions and end-to-end AI-enabled drug discovery. The latter requires time, effort and investment but is going to be the game changer in the long run.
New pharma and biotech players are scaling up fast and creating significant value using AI and big data, but the applications are diverse and pharma companies need to determine where and how AI can most add value for them. In order to make significant progress, pharma companies need to invest in data, technologies, and news skills and behaviors throughout the R&D organization.
It is tempting to think that AI can be delivered through a single team or a new tool but our experience says otherwise. Achieving full value from AI requires a transformation of the discovery process.
visionVision and Strategy.
Pharma companies need to develop a strategic AI roadmap that identifies specific, high-value use cases that are aligned with specific discovery programs.
Focus and prioritization are key: companies should identify a small number of use cases (typically, five to seven) spread across programs or stages of discovery. These use cases should directly reflect the company's R&D strategy or financial goals—otherwise, AI will be seen as a sideline.
Use cases must have support from senior leadership and “pull” from discovery and development teams.
Don’t wait for large data and technology platforms to become available to get started. Before building an entire tool or platform, focus on attaining a proof-of-concept algorithm: the minimum sufficient analysis that confirms your ability to extract valuable insights from your data in a specific scientific context.
If the insights are sufficiently valuable, you can then invest in industrializing the tool and adding a friendlier user interface.
At Moisaka, we work on a proof-of-concept algorithm first before building the entire platform based on the use case scenario. This method allows us to provide results before the next phase of investments.
For instance it takes about 18 weeks to build an AI-based tool (proof of concept) for optimizing the formulation conditions for proteins based on a combination of existing in-house data and externally available stability data - a typical timeframe for go or no-go decisions on a proof-of-concept algorithm.
AI offers major technological advances that may represent a paradigm shift in drug discovery and, ultimately, clinical development. We believe that advances that now feel like a sea change will rapidly become table stakes in discovery speed, novelty, and commercial potential. Many use cases are already maturing to the point where the impact is well understood. Acting boldly, with a clearly articulated strategy that resets a few key opportunities in your discovery efforts, can set you on the right path. The companies that move quickly will be the biggest winners.
If you’re interested in investing in drug discovery using AI, drop us an email info[at]moisaka.com to start the discussion on proof-of-concept and next steps.