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www.dreamfold.ai | ||
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www.blopig.com
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| | | | | [AI summary] This post explains how Graph Neural Networks and pre-trained protein language models are used to predict biological properties of proteins by modeling their structural and sequence data. | |
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scorpil.com
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| | | | | In Part One of the "Understanding Generative AI" series, we delved into Tokenization - the process of dividing text into tokens, which serve as the fundamental units of information for neural networks. These tokens are crucial in shaping how AI interprets and processes language. Building upon this foundational knowledge, we are now ready to explore Neural Networks - the cornerstone technology underpinning all Artificial Intelligence research. A Short Look into the History Neural Networks, as a technology, have their roots in the 1940s and 1950s. | |
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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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blog.fastforwardlabs.com
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| | | This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra. | ||
