  Accurate and robust prediction of drug-target interactions (DTIs) plays a
vital role in drug discovery. Despite extensive efforts have been invested in
predicting novel DTIs, existing approaches still suffer from insufficient
labeled data and cold start problems. More importantly, there is currently a
lack of studies focusing on elucidating the mechanism of action (MoA) between
drugs and targets. Distinguishing the activation and inhibition mechanisms is
critical and challenging in drug development. Here, we introduce a unified
framework called DTIAM, which aims to predict interactions, binding affinities,
and activation/inhibition mechanisms between drugs and targets. DTIAM learns
drug and target representations from large amounts of label-free data through
self-supervised pre-training, which accurately extracts the substructure and
contextual information of drugs and targets, and thus benefits the downstream
prediction based on these representations. DTIAM achieves substantial
performance improvement over other state-of-the-art methods in all tasks,
particularly in the cold start scenario. Moreover, independent validation
demonstrates the strong generalization ability of DTIAM. All these results
suggested that DTIAM can provide a practically useful tool for predicting novel
DTIs and further distinguishing the MoA of candidate drugs. DTIAM, for the
first time, provides a unified framework for accurate and robust prediction of
drug-target interactions, binding affinities, and activation/inhibition
mechanisms.
