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Accurate and robust prediction of drug-target interactions (DTIs) plays avital role in drug discovery. Despite extensive efforts have been invested inpredicting novel DTIs, existing approaches still suffer from insufficientlabeled data and cold start problems. More importantly, there is currently alack of studies focusing on elucidating the mechanism of action (MoA) betweendrugs and targets. Distinguishing the activation and inhibition mechanisms iscritical and challenging in drug development. Here, we introduce a unifiedframework called DTIAM, which aims to predict interactions, binding affinities,and activation/inhibition mechanisms between drugs and targets. DTIAM learnsdrug and target representations from large amounts of label-free data throughself-supervised pre-training, which accurately extracts the substructure andcontextual information of drugs and targets, and thus benefits the downstreamprediction based on these representations. DTIAM achieves substantialperformance improvement over other state-of-the-art methods in all tasks,particularly in the cold start scenario. Moreover, independent validationdemonstrates the strong generalization ability of DTIAM. All these resultssuggested that DTIAM can provide a practically useful tool for predicting novelDTIs and further distinguishing the MoA of candidate drugs. DTIAM, for thefirst time, provides a unified framework for accurate and robust prediction ofdrug-target interactions, binding affinities, and activation/inhibitionmechanisms.

DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms