Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models

Jin, Wengong; Barzilay, Regina; Jaakkola, Tommi

Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity. Recent approaches have applied machine learning to identify synergistic combinations for cancer, but they are not applicable to new diseases with limited combination data. Given that drug synergy is closely tied to biological targets, we propose a \emph{biological bottleneck} model that jointly learns drug-target interaction and synergy. The model consists of two parts: a drug-target interaction and target-disease association module. This design enables the model to \emph{explain} how a biological target affects drug synergy. By utilizing additional biological information, our model achieves 0.78 test AUC in drug synergy prediction using only 90 COVID drug combinations for training. We experimentally tested the model predictions in the U.S. National Center for Advancing Translational Sciences (NCATS) facilities and discovered two novel drug combinations (Remdesivir + Reserpine and Remdesivir + IQ-1S) with strong synergy in vitro.