A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions

Mechanistically predicting the consequences of drug action requires distin-guishing whether molecular interactions are activating or inhibitory, yet most biomedical knowledge graphs and graph neural networks represent biology as unsigned associations. This limitation obscures regulatory logic, restricts mechanistic interpretability and reduces the accuracy of downstream therapeutic predictions. Existing approaches are further constrained by limited chemical coverage and insufficient integration of molecular and clinical data across biological scales. Here we present SIGMA-KG (SIGned Multi-omics Atlas Knowledge Graph), a large-scale signed multi-omics knowledge atlas, and FLASH (Fast Lightwei