Expected Developments in Industry LLM Application

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.

What are the next big developments or breakthroughs you expect to see in the application of large language models to the pharmaceutical industry?

The ideal scenario that everybody is trying to get to is the ability to leverage the publicly available information, the public available sources, and combining them with a the, if you will, secretive because of competition within the different pharmaceutical companies, the secretive aspects of the clinical trials before they're actually published to expedite and to actually ameliorate the potential success of a clinical trial.

If there were an opportunity for companies to come together and leverage the combined knowledge without necessarily losing their competitive edge against one another. This would be probably the most revolutionary development in the use of this technology, because it would allow everybody to get faster. This would be beneficial, ultimately, for the patients. And this is, at the end of the day, why we do what we do. But it wouldn't take away that healthy competition between the pharmaceutical companies in trying to advance the molecules with the better outcomes.