ArticleComputational and structural biotechnology journal2026
Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization.
Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Explainable artificial intelligence approaches accelerate drug discovery by improving molecular representation learning, identifying key molecular structures, and rationalizing drug property prediction. However, developing end-to-end explainable models for structure-activity relationship modeling in target-specific compound property prediction remains challenging due to the limited availability of compound-protein interaction data for individual targets and the fact that small changes in chemical substituents or local structural motifs can lead to large differences in molecular properties. Thus, optimally leveraging structural and property information and identifying key moieties related to compound-protein affinity for specific targets is essential. We propose a framework implementing graph neural networks (GNNs) to leverage property and structure information from pairs of molecules with activity cliffs targeting specific proteins to predict compound-protein affinity (i.e., half-maximal inhibitory concentration, IC
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Registered trials
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