BACKGROUND Pathogenic variants in STXBP1/MUNC18-1 cause severe encephalopathies that are among the most common in genetic neurodevelopmental disorders. Different molecular disease mechanisms have been proposed, and pathogenicity prediction is limited. In this study, we aimed to define a generalized disease concept for STXBP1-related disorders and improve prediction. METHODS A cohort of 11 disease-associated and 5 neutral variants (detected in healthy individuals) were tested in 3 cell-free assays and in heterologous cells and primary neurons. Protein aggregation was tested using gel filtration and Triton X-100 insolubility. PRESR (predicting STXBP1-related disorder), a machine learning algorithm that uses both sequence- and 3-dimensional structure-based features, was developed to improve pathogenicity prediction using 231 known disease-associated variants and comparison to our experimental data. RESULTS Disease-associated variants, but none of the neutral variants, produced reduced protein levels. Cell-free assays demonstrated directly that disease-associated variants have reduced thermostability, with most variants denaturing around body temperature. In addition, most disease-associated variants impaired SNARE-mediated membrane fusion in a reconstituted assay. Aggregation/insolubility was observed for none of the variants in vitro or in neurons. PRESR outperformed existing tools substantially: Matthews correlation coefficient = 0.71 versus <0.55. CONCLUSIONS These data establish intrinsic protein instability as the generalizable, primary cause for STXBP1-related disorders and show that protein-specific ortholog and 3-dimensional information improve disease prediction. PRESR is a publicly available diagnostic tool.