SculptDrug : A Spatial Condition-Aware Bayesian Flow Model for Structure-based Drug Design

TL;DR

SculptDrug is a novel Bayesian flow model that generates protein-compatible drug molecules by incorporating spatial, boundary, and hierarchical structural constraints, improving accuracy in structure-based drug design.

Contribution

It introduces a spatial condition-aware Bayesian flow model with boundary and hierarchical encoders for improved ligand generation in SBDD.

Findings

Outperforms state-of-the-art baselines on CrossDocked dataset

Ensures spatial fidelity through progressive denoising

Effectively incorporates protein surface constraints

Abstract

Structure-Based drug design (SBDD) has emerged as a popular approach in drug discovery, leveraging three-dimensional protein structures to generate drug ligands. However, existing generative models encounter several key challenges: (1) incorporating boundary condition constraints, (2) integrating hierarchical structural conditions, and (3) ensuring spatial modeling fidelity. To address these limitations, we propose SculptDrug, a spatial condition-aware generative model based on Bayesian flow networks (BFNs). First, SculptDrug follows a BFN-based framework and employs a progressive denoising strategy to ensure spatial modeling fidelity, iteratively refining atom positions while enhancing local interactions for precise spatial alignment. Second, we introduce a Boundary Awareness Block that incorporates protein surface constraints into the generative process to ensure that generated ligands are geometrically compatible with the target protein. Third, we design a Hierarchical Encoder that captures global structural context while preserving fine-grained molecular interactions, ensuring overall consistency and accurate ligand-protein conformations. We evaluate SculptDrug on the CrossDocked dataset, and experimental results demonstrate that SculptDrug outperforms state-of-the-art baselines, highlighting the effectiveness of spatial condition-aware modeling.