Potential Disruptions to Current R&D Practices

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.

How do you see these models potentially disrupting current R&D practices in the pharma industry?

At the moment, I will say that the place where AI has made the biggest impact is definitely as also a buzz in the discovery aspect of clinical research, right? Being able to leverage computational power to streamline and identify promising candidates before going through the clinics has reduced the amount of time and definitely the amount of sources spent for the target notifications.

That said, despite a successful entry of AI made drugs into the pipeline, we haven't necessarily yet ripped the benefits of. There is no actual clinical asset that has made it to approval that it was generated through AI. This is the part of the space where Intelligentsia plays a bit in the clinical development. There are other applications that may be covered into actual simple optimization of the process: from a manufacturer standpoint, a supply chain standpoint, or a clinical trial design optimization perspective.