Effective High-Order Graph Representation Learning for Credit Card Fraud Detection

Abstract

Efficient generation of targeted drug molecules is crucial in the field of drug discovery. Most existing methods neglect the high-order information in the structure of protein pockets, limiting the performance of generated drug molecules. This paper proposes a pocket-aware drug design framework, namely PAMol, constructing the hypergraph to represent the spatial structure of protein pockets, effectively capturing high-order relations and neighborhood information within the pocket structures. This framework also fuses different modal embeddings from proteins and molecules, to generate high-quality molecules. In addition, a conditional molecule generation module uses the high-order structural information in protein pockets as constraints to more accurately generate molecules for specific targets. The performance of PAMol has been assessed by analyzing generated molecules in terms of vina score, high affinity, QED, SA, LogP, Lipinski, diversity, and time. Experimental results demonstrate the potential of PAMol for targeted drug design. The source code is available at https://github.com/YICHUANSYQ/PAMol.git.