KGDiff: towards explainable target-aware molecule generation with knowledge guidance.

ABSTRACT: Designing 3D molecules with high binding affinity for specific protein targets is crucial in drug design. One challenge is that the atomic interaction between molecules and proteins in 3D space has to be taken into account. However, the existing target-aware methods solely model the joint distribution between the molecules and proteins, disregarding the binding affinities between them, which leads to limited performance. In this paper, we propose an explainable diffusion model to generate molecules that can be bound to a given protein target with high affinity. Our method explicitly incorporates the chemical knowledge of protein-ligand binding affinity into the diffusion model, and uses the knowledge to guide the denoising process towards the direction of high binding affinity. Specificall

SUBMITTER: Qian H 

PROVIDER: S-EPMC10783868 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature