G–PLIP: Knowledge graph neural network for structure-free protein–ligand bioactivity prediction

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Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

Chaperones ensure protein homeostasis and are conserved across species. The ATP-dependent chaperone Hsp90 is present from bacteria to eukaryotes, where it stabilizes and activates a wide range of substrate proteins called clients. However, what determines ...

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Protein stabilization has a crucial rule sustaining biological functionality, disease prevention, and pharmaceutical formulation stabilization, and yet its stabilization mechanism by small molecules such as amino acids is not fully understood. In this work ...

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Proteins control nearly every facet of life on a molecular level. Proteins are formed from linear strings of amino acids, which fold into three-dimensional structures that can enact functions. Evolution has created highly efficient proteins in diverse fold ...

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The stochastic block model (SBM) is widely studied as a benchmark for graph clustering aka community detection. In practice, graph data often come with node attributes that bear additional information about the communities. Previous works modeled such data ...

G–PLIP: Knowledge graph neural network for structure-free protein–ligand bioactivity prediction

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