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A series of poly(1,4-dihydropyridine)s (PDHPs) were successfully synthesized via one-pot metal-free multicomponent polymerization of diacetylenic esters, benzaldehyde, and aniline derivatives. These PDHPs without traditional luminescent units were endowed with tunable triplet energy levels by through-space conjugation from the formation of different cluster sizes. The large and compact clusters can effectively extend the phosphorescence wavelength. The triplet excitons can be stabilized by using benzophenone as a rigid matrix to achieve room-temperature phosphorescence. The nonconjugated polymeric clusters can show a phosphorescence emission up to 645 nm. A combination of static and dynamic laser light scattering was conducted for insight into the structural information on formed clusters in the host matrix melt. Moreover, both the fluorescence and phosphorescence emission can be easily tuned by the variation of the excitation wavelength, the concentration, and the molecular weight of the guest polymers. This work provides a unique insight for designing polymeric host−guest systems and a new strategy for the development of long wavelength phosphorescence materials.

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The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.