BiAtt-GVAE: Molecular Design for Specific Target via Graph Variational Autoencoder Based on Bi-Channel Interactive Attention Network

Designing bioactive molecules with desired properties for specific targets is a longstanding challenge in drug design. We introduce a model called BiAtt-GVAE, which incorporates a conditional to more effectively constrain and enhance the generated molecules. To capture interaction sites between ligand atoms and protein amino acids, as well as the structure-properties relationship of ligands, we designed a bi-channel interactive attention network. The interactive attention block operates on the protein-ligand graph, facilitating the interactive learning of ligand atoms and protein amino acid features. We consider various properties to learn the ligand structure-properties relationship through a multi-head cross-attention block. Testing on EGFR and CDK2 targets, as well as through a dedicated case study for designing SARS-CoV-2 Mpro inhibitors demonstrates the effectiveness of BiAtt-GVAE. It has been verified through subsequent investigation of the molecular structure and molecular docking of Mpro inhibitors that BiAtt-GVAE can produce compounds with high ratings for both novelty and affinity.