From the Bulletin of the Atomic Scientists, August 25:
For more than two decades, lone actors with weapons of mass destruction ambitions have relied on crude toxins like ricin and amatoxins because plants like castor beans and mushrooms that contain these poisons fly under the export control radar. Law enforcement has uncovered more than a dozen plots to synthesize and use ricin. In contrast, there haven’t been any publicly documented amateur sarin or other nerve agent production attempts, not for lack of chemistry know-how but because Schedule 1 chemical precursors (substances that can be used to make weapons) are policed, flagged, and scarce. Acquiring these precursors even for peaceful research could only be done within official, institutional settings.
For those contemplating mass casualties, biotoxins like ricin are easy to produce. But delivering them effectively is difficult, requiring specialized techniques like aerosolization. Conversely for chemical warfare agents like sarin, synthesis (because of restricted precursors) has traditionally been the hard part, even if delivering them does not require specialized methods.
However, that balance is tipping because of advances in artificial intelligence. Generative AI and off-the-shelf computational tools are collapsing the precursor barrier, making DIY sarin analogs (compounds that resemble the molecular structure of sarin) as attractive as castor-bean mash for rogue individuals. These applications can make the synthesis and use of nerve agents for nefarious purposes marginally but consequentially more probable.
By enabling three-dimensional similarity searches, AI-guided planning of chemical synthesis routes, and predictions of how chemical warfare agents work in the body at a molecular level and at massive scale, these tools can identify unlisted but functionally equivalent precursors and products. Combined with large language model-based prompt engineering, today’s technologies can lower the obstacles to designing novel agents. Current regulatory barriers—including lists that categorize harmful substances—are not designed for this fast-moving frontier. Unless regulatory bodies evolve into adaptive systems that model the effects and not just the molecular structures of these compounds, the static lists of these organizations will lag dangerously behind technological advances. Those regulatory organizations can use AI, which is the instrument leading humans into this threat to begin with, to counter it.
Bypassing restrictions. There are several ways in which people could use AI and computational chemistry to bypass or creatively get around current control mechanisms. For instance, in 2022 researchers showed that machine learning software optimization functions that penalized certain toxic properties of molecules could instead reward the design of toxic analogs of the nerve agent VX. The software found over 40,000 analogs; assuredly more than a few of these would not appear on current scheduled lists and would be as or more toxic than VX.
During the last few years, there has been another significant development, namely AI-guided retrosynthesis. This computational technique uses AI to break down a target molecule into its building blocks. It can then suggest a synthetic route—complete with reaction conditions and reagents—for assembling these building blocks into the target molecule. Much like there are multiple ways to assemble Lego pieces into a house, these tools provide multiple alternative pathways to a molecule. Retrosynthesis can easily be used to deconstruct the structure of a nerve agent like sarin into unscheduled precursors that are invisible to the Chemical Weapons Convention or chemical supplier checklists....
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