Explainable artificial intelligence for targeted protein degradation predictions

Defining structure-activity relationships (SAR) is a central task in medicinal chemistry. Apart from optimizing activity against the target of interest, off-target activities and other properties need to be balanced to ensure a suitable property profile, which is an exceptional challenge in drug design. Machine learning (ML) can identify structural patterns in large compound collections that are correlated to biological activity or other molecular properties. Such ML-based SAR modeling has the potential of greatly assisting in compound optimization. However, the black-box character of most ML models has limited their application to help establishing SAR hypotheses. Explainable ML or, more generally, explainable artificial intelligence (XAI) aims at “opening the black box” by estimating how model inputs – e.g., chemical structures – contribute to model predictions. Although a variety of model interpretation methods have been proposed, XAI for medicinal chemistry is still an active field of research and XAI strategies are dominated by proofs of concept rather than by practical applications in drug discovery programs. Moreover, with the advent of new modalities, the applicability of ML and XAI models remains under-investigated. Herein, we present a novel application of XAI methods to targeted protein degradation (TPD) predictions. We report a case study of ML-based SAR modeling with explainable predictions of Cereblon (CRBN) glues for GSPT1 (G1 to S phase transition 1 protein). We showcase how XAI results were able to mirror expert knowledge based on structural data. Importantly, quantitative evaluations showed the ability of our ML/XAI workflow to accurately describe TPD activity cliffs across different proteins. These findings support use of the proposed XAI strategy to help rationalizing model predictions and illustrates how XAI methods can be exploited to balance SAR across different targets or properties for the new modality of TPDs.