  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.
