A new kind of AI algorithm—designed to work with a small amount of data—may be able to assist in the early stages of drug development. Artificial intelligence doesn’t work all that well in situations where there is very little data, such as drug development. A new technique called one-shot learning, that requires only a small number of data points might be a solution to that low-data problem. To make molecular information more digestible, the researchers first represented each molecule in terms of the connections between atoms (what a mathematician would call a graph). This step highlighted intrinsic properties of the chemical in a form that an algorithm could process. With these graphical representations, the group trained an algorithm on two different datasets—one with information about the toxicity of different chemicals and another that detailed side effects of approved medicines. From the first dataset, they trained the algorithm on six chemicals and had it make predictions about the toxicity of the other three. Using the second dataset, they trained it to associate drugs with side effects in 21 tasks, testing it on six more. In both cases, the algorithm was better able to predict toxicity or side effects than would have been possible by chance.
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