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Breast cancer is one of the most common cancer with high morbidity and mortality in women. This study aimed to explore the potential mechanism of costunolide inducing MCF‐7 cells apoptosis by multi‐spectroscopy, molecular docking, and cell experiments. The results manifested that costunolide interacted with calf thymus DNA (ct‐DNA) in a spontaneous manner, and the minor groove as the preferential binding mode. Furthermore, costunolide inhibited cell proliferation and colony formation. Hoechst 33258 staining showed that cell apoptosis induced by costunolide might be related to DNA damage. The apoptosis mechanism relied on regulating the protein expression of Bax, Bcl‐2, p53, Caspase‐3 and the activation of p38MAPK and nuclear factor κB (NF‐κB) pathways. This study will provide some experimental basis and potential therapeutic strategy for breast cancer treatment.

At present, drug toxicity has become a critical problem with heavy medical and economic burdens. acLQTS (acquired Long QT Syndrome) is acquired cardiac ion channel disease caused by drugs blocking the hERG channel. Therefore, it is necessary to avoid cardiotoxicity in the drug design and computer models have been widely used to fix this plight. In this study, we present a molecular fingerprint based on the molecular dynamic simulation and uses it combined with other molecular fingerprints (multi-dimensional molecular fingerprints) to predict hERG cardiotoxicity of compounds. 203 compounds with hERG inhibitory activity (pIC50) were retrieved from a previous study and predicting models were established using four machine learning algorithms based on the single and multi-dimensional molecular fingerprints. Results showed that MDFP has the potential to be an alternative to traditional molecular fingerprints and the combination of MDFP and traditional molecular fingerprints can achieve higher prediction accuracy. Meanwhile, the accuracy of the best model, which was generated by consensus of four algorithms with multi-dimensional molecular fingerprints, was 0.694 (RMSE) in the test dataset. Besides, the number of hydrogen bonds from MDFP has been determined as a critical factor in the predicting models, followed by rgyr and sasa. Our findings provide a new sight of MDFP and multi-dimensional molecular fingerprints in building models of hERG cardiotoxicity prediction.