A new algorithm trained on millions of reactions can tell scientists the steps required to build organic compounds.
How it works: Researchers at the University of Manchester trained a neural network on the 12.4 million single-step organic-chemistry reactions currently known to science. That data set lets the network predict more complicated multi-step chains of reactions that produce increasingly complex molecules, without having any chemistry rules hard-coded into the algorithm.
Double checking: In a double-blind study, the researchers gave 45 chemists two potential synthesis routes for creating nine different molecules—one each from the algorithm and a human scientist. According to Nature, the chemists didn’t prefer one over the other, and eight of the algorithm’s suggestions worked properly when tested in the laboratory.
Why it matters: Currently, chemists create these kinds of chemical recipes with painstaking research into existing processes (and a little intuition). That can take days. This new tool could speed up basic science in fields like drug discovery, freeing up chemists to do other work.
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