MGDEval: A Curated Multi-Endpoint Benchmark for Generalization in Molecular Glue Degrader Activity Prediction

Molecular glue degraders (MGDs) are small molecules that induce or stabilize interactions between target proteins and E3 ubiquitin ligases, leading to target ubiquitination and subsequent proteasomal degradation. Their activity depends on the specific molecular and protein context, yet experimentally reported bioactivity remains fragmented across public resources, limiting systematic computational evaluation. We introduce MGDEval, an integrated data resource and benchmark dedicated to MGD activity prediction. Through manual curation, MGDEval integrates 19,815 records, including 9,893 with quantitative activity information, while preserving biological context and source evidence. To assess generalization to new scaffolds and target proteins, we benchmark 45 representation–model configurations for classification and regression across DC50, Dmax, EC50, and IC50. Classification performance declined with increasing distribution shift: mean AUROC fell from 0.895 under random splitting to 0.839 under scaffold splitting and 0.561 under target-cluster splitting. Analyses of this gap showed reduced chemical and protein support under target-cluster splitting and limited transfer gains from current protein representations, motivating models that learn how molecules, targets, and effectors jointly determine activity. Exact-label activity measurements and cell-line information improved generalization, highlighting the importance of data quality and biological context for prediction on unseen targets. MGDEval provides a reusable data foundation and reproducible evaluation framework for advancing activity prediction and guiding molecular glue discovery across new chemical and target spaces.