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Dual use of artificial-intelligence-powered drug discovery

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  1. streblo

    An international security conference explored how artificial intelligence (AI) technologies for drug discovery could be misused for de novo design of biochemical weapons. A thought experiment evolved into a computational proof.

    We had previously designed a commercial de novo molecule generator that we called MegaSyn2, which is guided by machine learning model predictions of bioactivity for the purpose of finding new therapeutic inhibitors of targets for human diseases. This generative model normally penalizes predicted toxicity and rewards predicted target activity. We simply proposed to invert this logic by using the same approach to design molecules de novo, but now guiding the model to reward both toxicity and bioactivity instead. We trained the AI with molecules from a public database using a collection of primarily drug-like molecules (that are synthesizable and likely to be absorbed) and their bioactivities. We opted to score the designed molecules with an organism-specific lethal dose (LD50) model3 and a specific model using data from the same public database that would ordinarily be used to help derive compounds for the treatment of neurological diseases (details of the approach are withheld but were available during the review process). The underlying generative software is built on, and similar to, other open-source software that is readily available4. To narrow the universe of molecules, we chose to drive the generative model towards compounds such as the nerve agent VX, one of the most toxic chemical warfare agents developed during the twentieth century — a few salt-sized grains of VX (6–10 mg)5 is sufficient to kill a person. Other nerve agents with the same mechanism such as the Novichoks have also been in the headlines recently and used in poisonings in the UK and elsewhere6.

    3 votes