Graph neural networks for conditional de novo drug design

Abstract

Abstract Drug design is costly in terms of resources and time. Generative deep learning techniques are using increasing amounts of biochemical data and computing power to pave the way for a new generation of tools and methods for drug discovery and optimization. Although early methods used SMILES strings, more recent approaches use molecular graphs to naturally represent chemical entities. Graph neural networks (GNNs) are learning models that can natively process graphs. The use of GNNs in drug discovery is growing exponentially. GNNs for drug design are often coupled with conditioning techniques to steer the generation process towards desired chemical and biological properties. These conditioned graph‐based generative models and frameworks hold promise for the routine application of GNNs in drug discovery. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Chemoinformatics Data Science > Computer Algorithms and Programming

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