![]() In its study, the company’s scoring method revealed that many of the novel molecules MegaSyn generated were predicted to be more toxic than VX, a realization that made both Urbina and Ekins uncomfortable. A higher score tells researchers that the substance might be more likely to have the desired effect. The AI systems “score” a molecule based on certain criteria, such as how well it either inhibits or activates a specific protein. “Basically, the neural net is telling us which roads to take to lead to a specific destination, which is the biological activity,” says Alex MacKerell, director of the Computer-Aided Drug Design Center at the University of Maryland School of Pharmacy, who was not involved in the research. Many drug discovery AIs, including MegaSyn, use artificial neural networks. For their study, the researchers had merely asked the software to generate substances similar to VX without inputting the exact structure of the molecule. ![]() These drugs, called acetylcholinesterase inhibitors, can help treat neurodegenerative conditions such as Alzheimer’s. The researchers had previously used MegaSyn to generate molecules with therapeutic potential that have the same molecular target as VX, Urbina says. The eerie resemblance to the company’s day-to-day routine work was startling. ![]() “Our sense is that could form a useful springboard for policy development in this area,” says Filippa Lentzos, co-director of the Center for Science and Security Studies at King’s College London and a co-author of the paper. Urbina, Ekins and their colleagues even published a peer-reviewed commentary on the company’s research in the journal Nature Machine Intelligence-and went on to give a briefing on the findings to the White House Office of Science and Technology Policy. “It wasn’t any different from something we had done before-use these generative models to generate hopeful new drugs.”Ĭollaborations presented the work at Spiez CONVERGENCE, a conference in Switzerland that is held every two years to assess new trends in biological and chemical research that might pose threats to national security. “It just felt a little surreal,” Urbina says, remarking on how the software’s output was similar to the company’s commercial drug-development process. All it took was a bit of programming, open-source data, a 2015 Mac computer and less than six hours of machine time. The team ran MegaSyn overnight and came up with 40,000 substances, including not only VX but other known chemical weapons, as well as many completely new potentially toxic substances. It did not take long for them to come up with an idea: What if, instead of using animal toxicology data to avoid dangerous side effects for a drug, Collaborations put its AI-based MegaSyn software to work generating a compendium of toxic molecules that were similar to VX, a notorious nerve agent? In responding to the invitation, Sean Ekins, Collaborations’ chief executive, began to brainstorm with Fabio Urbina, a senior scientist at the company. The talk dealt with how artificial intelligence software, typically used to develop drugs for treating, say, Pitt-Hopkins syndrome or Chagas disease, might be sidetracked for more nefarious purposes. The private Raleigh, N.C., firm was asked to make a presentation at an international conference on chemical and biological weapons. In 2020 Collaborations Pharmaceuticals, a company that specializes in looking for new drug candidates for rare and communicable diseases, received an unusual request.
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