Finely manipulating room temperature phosphorescence by dynamic lanthanide coordination toward multi-level information security.

Room temperature phosphorescence materials have garnered significant attention due to their unique optical properties and promising applications. However, it remains a great challenge to finely manipulate phosphorescent properties to achieve desirable phosphorescent performance on demand. Here, we show a feasible strategy to finely manipulate organic phosphorescent performance by introducing dynamic lanthanide coordination. The organic phosphors of terpyridine phenylboronic acids possessing excellent coordination ability are covalently embedded into a polyvinyl alcohol matrix, leading to ultralong organic room temperature phosphorescence with a lifetime of up to 0.629 s. Notably, such phosphorescent performance, including intensity and lifetime, can be well controlled by varying the lanthanide dopant. Relying on the excellent modulable performance of these lanthanide-manipulated phosphorescence films, multi-level information encryption including attacker-misleading and spatial-time-resolved applications is successfully demonstrated with greatly improved security level. This work opens an avenue for finely manipulating phosphorescent properties to meet versatile uses in optical applications.

Targeting Compact and Ordered Emitters by Supramolecular Dynamic Interactions for High-performance Organic Ambient Phosphorescence.

Purely organic room-temperature phosphorescence (RTP) materials have received intense attention due to their fascinating optical properties and advanced optoelectronic applications. The promotion of intersystem crossing (ISC) and minimalization of nonradiative dissipation under ambient conditions are key prerequisites for realizing high-performance organic RTP; However, the ISC process is generally inefficient for organic fluorogens and the populated triplet excitons are always too susceptible to be well stabilized by conventional means. Particularly, organizing organic fluorophores into compact and ordered entities by supramolecular dynamic interactions has proven to be a newly-emerged strategy to boost the ISC process greatly and suppress the non-radiative relaxations immensely, facilitating the population and stabilization of triplet excitons to access high-performance organic RTP. Consequently, well-defined organic emitters enable robust RTP emission even in the solution state, thus greatly extending the applications. Here, this review is focused on a timely and brief introduction to recent progress in tailoring ordered high-performance RTP emitters by supramolecular dynamic interactions. Their typical preparation strategies, optoelectronic properties, and applications are thoroughly summarized. In the summary section, key challenges and perspectives of this field are highlighted to suggest potential directions for future study.

Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates.

Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolism of ∼7% of marketed drugs. The in vitro drug interaction studies guidance for industry issued by the FDA stipulates that drug sponsors need to evaluate whether the investigated drugs interact with the major drug-metabolizing P450s including CYP2B6. Therefore, there has been greater attention to the development of predictive models for CYP2B6 inhibitors and substrates. In this study, conventional machine learning and deep learning models were developed to predict CYP2B6 inhibitors and substrates. Our results showed that the best CYP2B6 inhibitor model yielded the AUC values of 0.95 and 0.75 with the 10-fold cross-validation and the test set, respectively, and the best CYP2B6 substrate model produced the AUC values of 0.93 and 0.90 with the 10-fold cross-validation and the test set, respectively. The generalization ability of the CYP2B6 inhibitor and substrate models was assessed by using the external validation sets. Several significant substructural fragments relevant to CYP2B6 inhibitors and substrates were detected via frequency substructure analysis and information gain. In addition, the applicability domain of the models was defined by employing a nonparametric method based on the probability density distribution. We anticipate that our results would be useful for the prediction of potential CYP2B6 inhibitors and substrates in the early stage of drug discovery.

A dinoflagellate-inspired mechanochromic film for fast and reversible information encryption and display.

Inspired by dinoflagellates, we developed a flexible film consisting of spiropyran-based soft polyacrylate and Zn(OTf)2. The open-ring form of spiropyran coordinated with Zn(OTf)2 under stretching to produce a visible fluorescent color change from colorless to yellow. The potential of this film was demonstrated for fast and reversible information encryption and decryption.

In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods.

Liver microsomal stability is an important property considered for the screening of drug candidates in the early stage of drug development. Determination of hepatic metabolic stability can be performed by an in vitro assay, but it requires quite a few resources and time. In recent years, machine learning methods have made much progress. Therefore, development of computational models to predict liver microsomal stability is highly desirable in the drug discovery process. In this study, the in silico classification models for the prediction of the metabolic stability of compounds in rat and human liver microsomes were constructed by the conventional machine learning and deep learning methods. The performance of the models was evaluated using the test and external sets. For the rat liver microsomes (RLM) stability, the best model yielded the AUC values of 0.84 and 0.71 on the test and external validation sets, respectively. For the human liver microsome (HLM) stability, the best model exhibited the AUC values of 0.86 and 0.77 on the test and external validation sets, respectively. In addition, several important substructure fragments were detected using information gain and frequency substructure analysis methods. The applicability domain of the models was defined using the Euclidean distance-based method. We anticipate that our results would be helpful for the prediction of liver microsomal stability of compounds in the early stage of drug discovery.