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Researchers have developed a groundbreaking AI framework that addresses critical challenges in peptide drug discovery. The GraphPep model leverages interaction-derived graph learning to significantly improve prediction accuracy for protein-peptide complexes.
Researchers have unveiled a novel artificial intelligence framework that reportedly transforms how scientists score protein-peptide interactions, according to recent publications in Nature Machine Intelligence. The new approach, named GraphPep, addresses fundamental limitations in peptide drug discovery by focusing specifically on interaction patterns rather than traditional structural elements.
Researchers have developed GraphComm, a graph-based deep learning method that predicts cell-cell communication from single-cell RNA sequencing data. The approach integrates ligand-receptor annotations with expression data to map interaction networks across biological systems. Validation studies demonstrate its utility in identifying communication patterns in embryonic development, cancer drug response, and spatial microenvironments.
Scientists have developed a novel graph-based deep learning method that reportedly predicts cell-cell communication (CCC) from single-cell RNA sequencing data, according to research published in Scientific Reports. The method, called GraphComm, leverages detailed ligand-receptor annotations alongside expression values and intracellular signaling information to construct interaction networks that can prioritize multiple interactions simultaneously.
A comprehensive study demonstrates that seemingly minor architectural choices in neural networks lead to fundamentally different circuit solutions for the same cognitive tasks. These differences significantly impact how networks handle unexpected inputs and generalize beyond their training data, with important implications for modeling biological intelligence.
According to research published in Nature Machine Intelligence, the selection of activation functions and connectivity constraints in recurrent neural networks (RNNs) leads to fundamentally different circuit mechanisms for solving identical cognitive tasks. The study analyzed six distinct RNN architectures using three common activation functions – ReLU, sigmoid, and tanh – with and without Dale’s law connectivity constraints, which restrict units to being exclusively excitatory or inhibitory like biological neurons.
