D²G-TO: Task-aware and OOD-guided Discrete Graph Diffusion for Robust CNS Drug Discovery

Abstract

Central nervous system (CNS) drug discovery is constrained by an immense and sparse chemical search space. Meanwhile, molecules that simultaneously achieve brain penetration, target efficacy, and synthesizability are extremely scarce. However, existing generative models rarely couple rigorous multi-objective control with robustness to distribution shift, limiting their reliability in realistic CNS design. We introduce D²G-TO, a task-aware and out of-distribution (OOD)-guided discrete graph diffusion framework that unifies multi-pharmacological properties with structural distribution guidance. A novel Structural Similarity Guidance mechanism steers generation toward in-distribution regions while repelling OOD modes, maintaining structural distributional consistency in realistic scenarios. Across BBBP, BACE, and QM9 benchmarks, D²G-TO achieves strong validity, diversity, and other metrics. In an Alzheimer's disease case study, we subject the generated molecules to cross-property pharmacological prediction and systematic ADMET profiling, followed by structure based molecular docking against BACE-1 to assess binding-mode plausibility. D²G-TO identifies candidates that jointly satisfy blood–brain barrier permeability, β-site amyloid precursor protein cleaving enzyme 1 inhibition, and synthetic accessibility. Thus, D²G-TO has the potential to serve as an efficient in silico engine for early-stage CNS drug design. The code is available at https://github.com/zhaix922/DDG_TO.