Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.

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Aims: Protection of cardiac function following myocardial infarction was largely enhanced by bradykinin-pretreated cardiac-specific c-kit+ (BK-c-kit+) cells, even without significant engraftment, indicating that paracrine actions of BK-c-kit+ cells play a pivotal role in angiogenesis. Nevertheless, the active components of the paracrine actions of BK-c-kit+ cells and the underlying mechanisms remain unknown. This study aimed to define the active components of exosomes from BK-c-kit+ cells and elucidate their underlying protective mechanisms. Methods and results: Matrigel tube formation assay, cell cycle, and mobility in human umbilical vein endothelial cells (HUVECs) and hindlimb ischemia (HLI) in mice were applied to determine the angiogenic effect of condition medium (CM) and exosomes. Proteome profiler, microRNA sponge, Due-luciferase assay, microRNA-sequencing, qRT-PCR, and Western blot were used to determine the underlying mechanism of the angiogenic effect of exosomes from BK-c-kit+. As a result, BK-c-kit+ CM and exosomes promoted tube formation in HUVECs and the repair of HLI in mice. Angiogenesis-related proteomic profiling and microRNA sequencing revealed highly enriched miR-3059-5p as a key angiogenic component of BK-c-kit+ exosomes. Meanwhile, loss- and gain-of-function experiments revealed that the promotion of angiogenesis by miR-3059-5p was mainly through suppression of TNFSF15 (VEGI)-inhibited effects on vascular tube formation, cell proliferation and cell migration. Moreover, enhanced angiogenesis of miR-3059-5p-inhibited TNFSF15 has been associated with Akt/Erk1/2/Smad2/3-modulated signaling pathway. Conclusion: Our results demonstrated a novel finding that BK-c-kit+ cells enrich exosomal miR-3059-5p to suppress TNFSF15 and promote angiogenesis against hindlimb ischemia in mice.