A neural network picks promising antibiotics from a library of chemicals


Biochemists have had some success designing drugs to meet specific goals. But much of drug development remains a tedious grind, screening hundreds to thousands of chemicals for a "hit" that has the effect you're looking for. There have been several attempts to perform this grind in silico, using computers to analyze chemicals, but they had mixed results. Now, a US-Canadian team reports that it modified a neural network to deal with chemistry and used it to identify a potential new antibiotic. Two factors greatly influence the success of neural networks: the structure of the network itself and the training it undergoes.

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