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www.dreamfold.ai | ||
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deepmind.google
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| | | | | New AI system designs proteins that successfully bind to target molecules, with potential for advancing drug design, disease understanding and more. | |
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www.blopig.com
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| | | | | [AI summary] The article discusses the application of graph neural networks (GNNs) in protein property prediction, highlighting their ability to model protein structures and interactions, the integration of pre-trained protein language models like ESM, and the use of residual layers to address oversmoothing challenges. | |
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blog.fastforwardlabs.com
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| | | | | Graph Neural Networks (GNNs) are neural networks that take graphs as inputs. These models operate on the relational information in data to produce insights not possible in other neural network architectures and algorithms. While there is much excitement in the deep learning community around GNNs, in industry circles, this is sometimes less so. So, I'll review a few exciting applications empowered by GNNs. Overview of Graphs and GNNs A graph (sometimes called a network) is a data structure that highlights the relationships between components in the data. | |
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www.techradar.com
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| | | AI images are damn popular right now | ||
