Consider the case of Atomwise, a startup firm which is developing novel technology for
identifying potential drug candidates (and insecticides) by using neural networks to predict the
bioactivity of candidate molecules. The company reports that its deep convolutional neural
networks “far surpass” the performance of conventional “docking” algorithms. After appropriate
training on vast quantities of data, the company’s AtomNet product is described as being able to
“recognize” foundational building blocks of organic chemistry, and is capable of generating
highly accurate predictions of the outcomes of real-world physical experiments (Wallach et al.,
2015).
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