Molecule generation for target protein binding with hierarchical consistency diffusion model.

ABSTRACT: Effective generation of molecular structures that bind to target proteins is crucial for lead identification and optimization in drug discovery. Despite advancements in atom- and motif-wise models for 3D molecular generation, current methods often struggle with validity and reliability. To address these issues, we develop the Atom-Motif Consistency Diffusion Model (AMDiff), utilizing a joint-training paradigm for multi-view learning. This model features a hierarchical diffusion architecture that integrates both atom- and motif-views of molecules, allowing for comprehensive exploration of complementary information. By leveraging classifier-free guidance and incorporating topological features as conditional inputs, AMDiff ensures robust molecule generation across diverse targets. Compared to

SUBMITTER: Li G 

PROVIDER: S-EPMC12418249 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature