A machine learning-based QSAR model reveals important molecular features for understanding the potential inhibition mechanism of ionic liquids to acetylcholinesterase.

The broad application of ionic liquids (ILs) has been hindered by uncertainties surrounding their ecotoxicity. In this work, a Quantitative Structure-Activity Relationship (QSAR) model was devised to predict the inhibition of ILs towards the activity of AChE, employing both Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) machine learning approaches. Fourteen kings of essential molecular feature descriptors were screened from an initial roster of 244 descriptors through the application of a feature importance index and they showed a significant impact on the activity of AChE activity.

Cellular uptake and cytotoxicity of PEGylated MXene nanomaterials mediated by protein corona.

A stringent analysis of the biocompatibility of MXene is a necessary condition for assessing the biological risk of MXene. Owing to high surface free energy, MXene is capable of adsorbing a large amount of blood proteins to form MXene-protein corona complexes, however, a comprehensive understanding of the relationship between MXene and cellular physiological systems remains limited. Therefore, we investigated the cellular uptake and cytotoxicity effect of MXene TiCT and PEGylation TiCT mediated by human serum protein corona in THP-1 cells.