Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases.However,the economic burden associated with detecting drug off-targets and potential side effects t...Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases.However,the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial.Drug off-target interactions,along with the adverse drug reactions they induce,are significant factors affecting drug safety.To assess the liability of candidate drugs,we developed an artificial intelligence model for the precise prediction of compound off-target interactions,leveraging multi-task graph neural networks.The outcomes of off-target predictions can serve as representations for compounds,enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity.Furthermore,the predicted off-target profiles are employed in adverse drug reaction(ADR)enrichment analysis,facilitating the inference of potential ADRs for a drug.Using the withdrawn drug Pergolide as an example,we elucidate the mechanisms underlying ADRs at the target level,contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions.Overall,our work facilitates the early assessment of compound safety/toxicity based on off-target identification,deduces potential ADRs of drugs,and ultimately promotes the secure development of drugs.展开更多
基金supported by National Key Research and Development Program of China(2022YFC3400504 to Mingyue Zheng)National Natural Science Foundation of China(T2225002 and 82273855 to Mingyue Zheng,82204278 to Xutong Li)+2 种基金Lingang Laboratory(LG202102-01-02 to Mingyue Zheng)SIMMSHUTCM Traditional Chinese Medicine Innovation Joint Research Program(E2G805H to Mingyue Zheng)Shanghai Municipal Science and Technology Major Project.
文摘Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases.However,the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial.Drug off-target interactions,along with the adverse drug reactions they induce,are significant factors affecting drug safety.To assess the liability of candidate drugs,we developed an artificial intelligence model for the precise prediction of compound off-target interactions,leveraging multi-task graph neural networks.The outcomes of off-target predictions can serve as representations for compounds,enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity.Furthermore,the predicted off-target profiles are employed in adverse drug reaction(ADR)enrichment analysis,facilitating the inference of potential ADRs for a drug.Using the withdrawn drug Pergolide as an example,we elucidate the mechanisms underlying ADRs at the target level,contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions.Overall,our work facilitates the early assessment of compound safety/toxicity based on off-target identification,deduces potential ADRs of drugs,and ultimately promotes the secure development of drugs.
