the one is recording the other
In less than 6 hours after starting on our in-house server, our model generated 40,000 molecules that scored within our desired threshold. In the process, the AI designed not only VX, but also many other known chemical warfare agents that we identified through visual confirmation with structures in public chemistry databases. Many new molecules were also designed that looked equally plausible. These new molecules were predicted to be more toxic, based on the predicted LD50 values, than publicly known chemical warfare agents (Fig. 1). This was unexpected because the datasets we used for training the AI did not include these nerve agents. The virtual molecules even occupied a region of molecular property space that was entirely separate from the many thousands of molecules in the organism-specific LD50 model, which comprises mainly pesticides, environmental toxins and drugs (Fig. 1). By inverting the use of our machine learning models, we had transformed our innocuous generative model from a helpful tool of medicine to a generator of likely deadly molecules.[...]
Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community. Although some domain expertise in chemistry or toxicology is still required to generate toxic substances or biological agents that can cause significant harm, when these fields intersect with machine learning models, where all you need is the ability to code and to understand the output of the models themselves, they dramatically lower technical thresholds. Open-source machine learning software is the primary route for learning and creating new models like ours, and toxicity datasets9 that provide a baseline model for predictions for a range of targets related to human health are readily available.
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