AI Drug Discovery Impact on Pharmaceutical Value Chain

In this interview, Edoardo Madussi, Head of Business Development, Intelligencia AI discusses the potential impact of AI-driven drug discovery platforms like DeepSeek and Qwen, highlighting their democratizing potential while also acknowledging challenges related to data quality and validation. The conversation explores the potential disruptions to current R&D practices, including the acceleration of drug discovery and the optimization of manufacturing and supply chains.

The discussion also addresses the potential risks associated with relying heavily on open-access AI models, including data security, intellectual property concerns, and the potential for biases in underlying datasets. Finally, the interview touches upon the environmental impact of AI, emphasizing the energy consumption of large language models while acknowledging the potential for AI to improve efficiency and reduce the environmental footprint of drug development.

Beyond drug discovery, where else in the pharmaceutical value chain do you see these models having the most significant impact?

This may be tied into my previous answer. They may actually be the ones that we're able to implement in a quicker fashion. That is to say, those applications may require stringent compliance because of the more process driven component or aspects of the AI, definitely. For instance, in an environment of production, of manufacturing, using AI to standardize some of the methodologies, using predictive power to learn from production times in the past and identify where, from a manufacturing standpoint, there may be efficiencies. These are simple fixes that AI can probably boost if from a upstream perspective. Looking back to the drug discovery, these may require much more careful validation as they are much more impactful on the potential outcome that have the results on the patients.