This study presents an integrated computational modeling framework combining deep learning and Quantitative Systems Pharmacology (QSP) to predict the efficacy of PROTAC (PROteolysis Targeting Chimera) molecules. PROTACs have emerged as promising therapeutics for targeted protein degradation (TPD), offering significant advantages in addressing proteins that traditional small-molecule inhibitors cannot target. However, experimental evaluation of PROTAC efficacy is hindered by extensive variability in molecular configurations, necessitating efficient computational prediction methods. The proposed model integrates binding affinity predictions from DeepCalici, a convolutional neural network-based deep learning model, with a mechanistic QSP Hook model to estimate key pharmacodynamic parameters, notably half-maximal degradation concentration( \(DC_{50}\) ) and maximal degradation( \(D_{max}\) ). This study utilized curated experimental data from PROTAC-DB, including experimentally validated \(DC_{50}\) and \(D_{max}\) values. The dissociation constants ( \(K_d\) ) between PROTAC molecules and their protein targets (POI) or E3 ligases were predicted using DeepCalici and, then incorporated into the Hook model. To enhance the prediction accuracy, a supplementary deep neural network adjusted the hook model parameters based on chemical and biochemical features. The integrated modeling approach achieved a strong predictive performance for \(DC_{50}\) , demonstrating its practical value in prioritizing effective PROTAC candidates. However, the predictions for \(D_{max}\) were less accurate, likely reflecting the variability in the experimental conditions not captured in the current dataset. This study highlights the critical importance of comprehensive structural data for accurate modeling of PROTAC efficacy and suggests future improvements using standardized experimental data. Such integrative modeling approaches promise to accelerate the discovery and optimization of PROTAC therapeutics.
