M3SpaDE: A Multi-Modal Deep Learning Framework for Predicting Spatially Resolved Drug Responses

M3SpaDE (Multi-Modal Model for predicting Spatial Drug Efficacy) is a versatile computational framework designed for predicting drug sensitivity in spatial transcriptomics data. It is resolution-agnostic, capable of processing data ranging from single-cell to spot-level resolutions, and supports generalizable prediction of responses to previously unseen drugs based on their chemical structures. M3SpaDE enables the following tasks: Binarized Sensitivity PredictionPerforms binary classification of drug sensitivity at the single-cell or spot level (Sensitive vs. Resistant). Spatial Autocorrelation AnalysisQuantifies global spatial dependency and clustering patterns using Join Count statistics. Combinatorial Therapy AssessmentPredicts and evaluates drug sensitivity outcomes for drug combinations.