Predicting Degradation Potential of Protein Targeting Chimeras

Abstract

PRoteolysis TArgeting Chimeras (PROTACs) can inhibit protein activity by utilizing natural proteasomal degradation pathways for the degradation of target proteins. Being able to determine the degradation potential of PROTACs is crucial in drug development as it can lead to time, labor and cost savings. In this paper, we present a novel machine-learning pipeline that utilizes common compound fingerprints and a pre-trained graph neural network for the prediction of half-maximal degradation concentration of PROTACs by benchmarking a variety of protein tertiary structures and chemical features. Based on critical analysis of our cross-validation and independent test results, we have highlighted several key challenges underlying this prediction problem that need to be addressed to improve the generalization of predictive models in this domain. Moreover, we demonstrate the effectiveness of our approach by testing it on two different datasets and show that it performs better than the current state of the art with an AUC-ROC of 0.85 and accuracy of 0.875 on the DeepPROTACs test dataset.