4.7 Article

Deep Evolutionary Learning for Molecular Design

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Summary: Geometric deep learning, based on neural network architectures that process symmetry information, shows promise in molecular modeling applications. This review provides a detailed overview of molecular GDL, its applications, challenges, and future opportunities, highlighting the importance of geometric representations in molecular deep learning. Kenneth Atz and colleagues review the current progress and challenges of geometric deep learning in molecular sciences, emphasizing the significance of spatial structure information in molecules.

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