Evaluating AI Solutions to Accelerate Drug Discovery
Takeda Pharmaceuticals
SUMMARY
Takeda wanted to explore the potential for AI/ML to accelerate their drug discovery process.
We applied our technological expertise to evaluate the current landscape of AI solutions in the marketplace.
Our approach was informed by a thorough understanding of Takeda’s drug discovery workflow and pain points.
We delivered a database of 500+ vendors, ranked and vetted by Takeda’s criteria and our evaluation, as well as detailed research memos for the most promising solutions.
Takeda’s executive team found great value in this project and engaged Hop for additional work toward their drug development process.
THE COMPANY
Takeda Pharmaceuticals Ltd is one of the oldest and largest pharmaceutical companies in the world, focused on oncology, neuroscience, gastroenterology, immunology and plasma products. At the time Takeda engaged Hop’s services, a team of executives across the R&D and technology sectors of the organization was exploring the potential of machine learning and artificial intelligence to remove friction in, and accelerate, the standard lengthy and rigorous drug discovery process.
THE CHALLENGE
As Takeda’s primary domain is medicine and pharmaceuticals, they sought Hop’s expertise on the technology side. Their key question: is there a role for AI and ML to play in accelerating the drug discovery process within Takeda? Our challenge was to understand Takeda’s processes and pain points and use those to guide our evaluation of the current landscape of AI solutions in the marketplace to find those that might be a good fit.
“One thing that’s great about working with Hop is the flexibility. We were short on time for this project. Hop was able to shortcut their standard process to quickly produce satisfactory results that we can now use to better understand Takeda’s needs and next steps.”
THE APPROACH
Our approach started with gaining an understanding from a variety of stakeholders of their workflows and pain points around specific parts of the drug discovery pipeline. From that understanding, we then conducted an external industry scan, using direct and indirect signals to assess viability and filter out AI solutions in the drug discovery space that were unlikely to be a fit for Takeda’s needs. For a filtered subset, we then considered a multitude of qualitative factors – the maturity of the team, the soundness of the research approach, their prior track record, and other organizational and technical factors – to prioritize the landscape for further investigation.
Throughout the process of building and filtering the database of solutions, Hop actively engaged in conversations with the Takeda team to ensure our choices were aligned with their priorities, and to incorporate Takeda’s prior internal experience into our evaluation. For the most promising solutions in our database, we executed a deeper evaluation of their technologies and teams, producing detailed research reports to inform Takeda executives of not just pros and cons of each solution, but particular areas of concern for engagement and recommendations for structuring a relationship with those organizations.
THE RESULTS
Our final deliverables to Takeda for this phase of work were a ranked and vetted database of 500+ AI solutions in the marketplace. These solutions were cross-referenced by Takeda’s needs and pain points, and by our own evaluation of the maturity of their team and the soundness of their technology. We also authored detailed research memos with deep-dive evaluations of the most promising solutions for Takeda’s needs.
Before the deliverables were complete, Takeda executives were already leveraging this work to inform strategic decisions regarding AI and potential partners and vendors. Hop is glad to know that this work has been valuable, and a subsequent project toward another aspect of Takeda’s drug development process is already underway.
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