Eraneos was asked by one of the largest multinational pharmaceutical companies if we could help it with its drug development process as it was taking longer than desired. At the time, drug development processes had no connection to the end consumer, and data exchange was becoming a regulatory requirement in more and more countries, enabling a variety of use cases. And so we got to work to find a more efficient way the company could do its drug discovery business.
We kicked off the project by conducting a number of interviews and workshops. This was to help gather the necessary requirements for AI-enabled drug design from chemists, stakeholders, and potential users. Next, we created user journeys and user stories and designed a visual mockup for an innovative, web-based user interface as well as the technical architecture of a cloud-native solution. We also created a product vision and implementation roadmap for a custom-built solution. On top of this, we designed machine learning libraries and ML Ops pipelines, evaluated the implementation options for 3D visualization of molecules, and prepared RFP documents.
As a result of our work on this project, the pharmaceutical company received a clear product vision and solution design alongside a transparent effort estimate and implementation timelines. We also increased the adoption of machine learning usage in its drug discovery process, speeding it up significantly. Consequently, this enabled new business models and additional revenue streams for the company.