Drug discovery with explainable artificial intelligence release_vwbm5ctaengetbsrkqjf54hoei

by José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider

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2020  

Abstract

Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke properties. Despite the growing number of successful prospective applications, the underlying mathematical models often remain elusive to interpretation by the human mind. There is a demand for 'explainable' deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and dares a forecast of the future opportunities, potential applications, and remaining challenges.
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Date   2020-07-02
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arXiv  2007.00523v2
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