[AI Supercharges Fentanyl Production And Distribution, Study Finds](https://wbbsec.com/opinions-and-features/ai-supercharges-fentanyl-production-and-distribution-study-finds/)
[AI Supercharges Fentanyl Production And Distribution, Study Finds](https://wbbsec.com/opinions-and-features/ai-supercharges-fentanyl-production-and-distribution-study-finds/)
In: Opinions & Features

4.27.2024 | Forbes

By Steve Brozak

A team of global experts in AI and synthetic chemistry have just published their research into AI’s capacity to identify hundreds of ways to scale manufacturing of fentanyl that evade current DEA controls. The implications are striking.

The study’s implications suggest that if only a fraction of the information provided progresses to its logical conclusion, the already out-of-control fentanyl crisis, will be supercharged and could present an imminent threat—a real-life Breaking Bad.

What makes AI-assisted illegal drug synthesis so dangerous, according to the Chem-Catalysis piece is that, using decades old AI technology, it is possible to identify scores of pathways for production of fentanyl using readily available precursor materials. These AI-algorithmically scripted formulas or “recipes” are extraordinarily inexpensive to “cook.”

The lead author, Dr. Bartosz A. Grzybowski, Distinguished Professor, Department of Chemistry, UNIST Director, Center for Algorithmic and Robotized Synthesis (CARS), Institute for Basic Science based in Ulsan, Republic of Korea, stated in a phone interview: “We did this piece as a first step in alerting governmental bodies and the global public as to the current power of AI in drug manufacturing. AI’s power is by no means limited to drug manufacturing and we should now begin the process of understanding the positive and negative possibilities of AI technologies.”

Dr. Grzybowski and his coauthors are chemists and AI specialists who have worked on the development of chemical AI for over a decade. According to Grybowski, setting up the computer protocol took only two days and produced the machine-learning solutions in 20-minutes. “The longest part of the effort was writing the journal article, which took two-months.”

To read the entire article on Forbes, please click here…