Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC50)of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal"dead ends"and"safe bets"for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogen-oxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.

Objective:To make an empirical study on verifying whether the self-developed simple laparoscopic simulator can play a good role in the training of laparoscopic skills.Methods:Twenty-four Batch 2018 and Batch 2019 undergraduates of five-year clinical medicine of the Army Medical University were recruited in this study, and they were randomly divided into a research group and a control group for 4 weeks of simulation training, 3 times a week, 1 class hour each time. The training content was fundamental laparoscopic skills (FLSs). Before and after the training, the assessment was performed and the results and completion time were recorded. After the training, a satisfaction questionnaire was conducted among all trainees. SPSS 22.0 was used for data statistical analysis.Results:There was no statistical difference between the completion time and assessment results of the two groups before training ( P > 0.05). After four weeks of training, the completion time of the research group was shortened by 10.03% and the training performance increased by 35.17%; the completion time of the control group was shortened by 2.09%, and the training performance improved 12.34%. The comparison between groups found that the research group was superior than the control group, and the performance of the two groups were all improved after training. Most trainees recognized the simulator and were willing to promote it to other trainees according to the questionnaire feedback. Conclusion:The original laparoscopic simulator has a better training effect on simulation training than the traditional apprenticeship teaching has, which is of great significance for the promotion and optimization of laparoscopic skills teaching.