Peptide‐Bridged Naphthalimide–Dithienylethene Dyad with Aggregation‐Induced‐Emission Activity: Application in Forensic Fingerprint Technology

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Peptide-Bridged Naphthalimide–Dithienylethene Dyad with Aggregation-Induced-Emission Activity: Application in Forensic Fingerprint Technology

Design and synthesize: A new class of photoswitchable materials integrating photochromic properties with aggregation-induced emission behavior via supramolecular self-assembly was developed. The materials (unsymmetrical peptide-bridged naphthalimide–dithienylethene dyads) display fluorescence photoswitching performance in solution, solid, and gel states. One of the developed dithienylethene-based materials was applied in fingerprint detection and in anti-counterfeiting technology.


Abstract

We demonstrate here a simple approach to integrate photochromic properties with aggregation-induced emission behavior via supramolecular self-assembly with the aim to build a new type of photoswitchable materials. We have designed and synthesized two unsymmetrical peptide-bridged naphthalimide–dithienylethene dyads, each composed of naphthalimide (NI), an alkyl (CH2)n [n=2,8] chain (Cn), a dipeptide of phe-phe scaffold, and an unsymmetrical dithienylethene (DTE) moiety (NI-Cn-pep-DTE; 6: n=2; 7: n=8). Dyads 6 and 7 show comparable photo-isomerization speed and rate constant (K) values for cyclization (75 s, K=0.049 s−1 for 6, 65 s, K=0.056 s−1 for 7) and cycloreversion (105 s, K=0.037 s−1 for 6, 100 s, K=0.023 s−1 for 7) accompanied by a noticeable naked-eye color change from pale yellow (6 o/7 o; open forms) to purple (6 c/7 c; closed forms). Both compounds show considerably high fatigue resistance for at least 45 cycles without loss of sensitivity and compound 7 exhibits fluorescence photoswitching performance in solution, solid state, as well as in gel form through a FRET mechanism. The developed dithienylethene (DTE)-based material was applied in latent fingerprints (LFPs) and in anti-counterfeiting technology in a non-invasive manner.

Predicting GPR40 Agonists with A Deep Learning‐Based Ensemble Model

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Predicting GPR40 Agonists with A Deep Learning-Based Ensemble Model

Various GPR40 agonists and non-agonists for model training and evaluation were collection and used for building and systematically optimizing an ensemble model for predicting GPR40 agonists. The ensemble model was built based on 20 baseline models, which were consisting of different algorithms and molecular representations. And the ensemble model showed greater performance than the performance of any baseline model.


Abstract

Recent studies have identified G protein-coupled receptor 40 (GPR40) as a promising target for treating type 2 diabetes mellitus, and GPR40 agonists have several superior effects over other hypoglycemic drugs, including cardiovascular protection and suppression of glucagon levels. In this study, we constructed an up-to-date GPR40 ligand dataset for training models and performed a systematic optimization of the ensemble model, resulting in a powerful ensemble model (ROC AUC: 0.9496) for distinguishing GPR40 agonists and non-agonists. The ensemble model is divided into three layers, and the optimization process is carried out in each layer. We believe that these results will prove helpful for both the development of GPR40 agonists and ensemble models. All the data and models are available on GitHub. (https://github.com/Jiamin-Yang/ensemble_model)