New AI approach bridges the ‘slim-data gap’ that can stymie deep learning approaches

Read the full story from Pacific Northwest National Laboratory.

Scientists have developed a deep neural network that sidesteps a problem that has bedeviled efforts to apply artificial intelligence to tackle complex chemistry – a shortage of precisely labeled chemical data. The new method gives scientists an additional tool to apply deep learning to explore drug discovery, new materials for manufacturing, and a swath of other applications.

Predicting chemical properties and reactions among millions upon millions of compounds is one of the most daunting tasks that scientists face. There is no source of complete information from which a deep learning program could draw upon. Usually, such a shortage of a vast amount of clean data is a show-stopper for a deep learning project.

Scientists at the Department of Energy’s Pacific Northwest National Laboratory discovered a way around the problem. They created a pre-training system, kind of a fast-track tutorial where they equip the program with some basic information about chemistry, equip it to learn from its experiences, then challenge the program with huge datasets.

The work was presented at KDD2018, the Conference on Knowledge Discovery and Data Mining, in London.

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