Orthogonal Molecular Feature Signatures Guide Multi-Target Alzheimer’s Drug Discovery Through Graph Transformer Representation Learning

Background: Single-target Alzheimer’s disease (AD) therapies have repeatedly failed to modify disease progression, highlighting a critical mismatch between multifactorial pathology and reductionist pharmacology. Methods: We developed a representation learning framework using Knowledge-guided Pre-trained Graph Transformers (KPGT) to enable rational multi-target drug discovery, analyzing 2446 molecules across APP, PSEN1, and VCP. Results: KPGT captured target-specific mechanistic signatures with 99.35% classification accuracy. Geometric midpoint analysis identified 15 bridging candidates with mean pIC50 8.09. We discovered two orthogonal molecular feature signatures, structural features driving multi-target breadth versus chemical features determining single-target potency, with zero descriptor overlap. Chemical orthogonality (d = 3.86) outperformed functional similarity for predicting synergistic pairs, with 95% overlap between multi-target molecules and synergistic combinations. Conclusions: This framework operationalizes systems-level AD drug discovery through interpretable representation learning.