Artificial intelligence in drug discovery: recent advances and future perspectives
José Jiménez-LunaDepartment of Chemistry and Applied Biosciences, ETH Zurich, Zurich, SwitzerlandFrancesca GrisoniDepartment of Chemistry and Applied Biosciences, ETH Zurich, Zurich, SwitzerlandNils WeskampBoehringer Ingelheim Pharma GmbH & Co. KG, Biberach an Der Riss, GermanyPetra SchneiderDepartment of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
2021en
ABI
Abstract
: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
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