Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
arxiv(2023)
摘要
In recent years, AI-assisted drug design methods have been proposed to
generate molecules given the pockets' structures of target proteins. Most of
them are atom-level-based methods, which consider atoms as basic components and
generate atom positions and types. In this way, however, it is hard to generate
realistic fragments with complicated structures. To solve this, we propose
D3FG, a functional-group-based diffusion model for pocket-specific molecule
generation and elaboration. D3FG decomposes molecules into two categories of
components: functional groups defined as rigid bodies and linkers as mass
points. And the two kinds of components can together form complicated fragments
that enhance ligand-protein interactions.
To be specific, in the diffusion process, D3FG diffuses the data distribution
of the positions, orientations, and types of the components into a prior
distribution; In the generative process, the noise is gradually removed from
the three variables by denoisers parameterized with designed equivariant graph
neural networks. In the experiments, our method can generate molecules with
more realistic 3D structures, competitive affinities toward the protein
targets, and better drug properties. Besides, D3FG as a solution to a new task
of molecule elaboration, could generate molecules with high affinities based on
existing ligands and the hotspots of target proteins.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要