TransDiffSBDD: Causality-Aware Multi-Modal Structure-Based Drug Design
This work addresses drug discovery for pharmaceutical applications, presenting a novel integrated framework.
The paper tackles structure-based drug design by addressing multi-modal challenges and causal relationships between molecular graphs and 3D coordinates, resulting in TransDiffSBDD outperforming existing baselines on the CrossDocked2020 benchmark.
Structure-based drug design (SBDD) is a critical task in drug discovery, requiring the generation of molecular information across two distinct modalities: discrete molecular graphs and continuous 3D coordinates. However, existing SBDD methods often overlook two key challenges: (1) the multi-modal nature of this task and (2) the causal relationship between these modalities, limiting their plausibility and performance. To address both challenges, we propose TransDiffSBDD, an integrated framework combining autoregressive transformers and diffusion models for SBDD. Specifically, the autoregressive transformer models discrete molecular information, while the diffusion model samples continuous distributions, effectively resolving the first challenge. To address the second challenge, we design a hybrid-modal sequence for protein-ligand complexes that explicitly respects the causality between modalities. Experiments on the CrossDocked2020 benchmark demonstrate that TransDiffSBDD outperforms existing baselines.