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Abstract Natural products (NPs) are a valuable source for anti-inflammatory drug discovery. However, they are limited by the unpredictability of the structures and functions. Therefore, computational and data-driven pre-evaluation could enable more efficient NP-inspired drug development. Since NPs possess structural features that differ from synthetic compounds, models trained with synthetic compounds may not perform well with NPs. There is also an urgent demand for well-curated databases and user-friendly predictive tools. We presented a comprehensive online web platform (InflamNat, http://www.inflamnat.com/ or http://39.104.56.4/ ) for anti-inflammatory natural product research. InflamNat is a database containing the physicochemical properties, cellular anti-inflammatory bioactivities, and molecular targets of 1351 NPs that tested on their anti-inflammatory activities. InflamNat provides two machine learning-based predictive tools specifically designed for NPs that (a) predict the anti-inflammatory activity of NPs, and (b) predict the compound-target relationship for compounds and targets collected in the database but lacking existing relationship data. A novel multi-tokenization transformer model (MTT) was proposed as the sequential encoder for both predictive tools to obtain a high-quality representation of sequential data. The experimental results showed that the proposed predictive tools achieved an AUC value of 0.842 and 0.872 in the prediction of anti-inflammatory activity and compound-target interactions, respectively.

Nanomaterials with various dimensionalities (e.g., nanowires, nanofilms, two-dimensional materials, and three-dimensional nanostructures) have shown great potential in the recent development of flexible electronics. Conventionally, organic solvents are inevitable while integrating nanomaterials onto flexible substrates, where polymer mediator-assisted transfer techniques are involved. This often damages the flexible substrate and thus hamper the large-scale application of nanomaterials. Here we report a method using water-soluble sugar as a mediator to facilely transfer nanomaterials onto rigid or flexible substrates. This method requires no organic solvent during transfer. More importantly, the morphology and properties of transferred nanomaterials, such as shape, microstructure, resistivity, and transmittance are well preserved on the target substrate. We believe that this universal and rapid transfer method can greatly advance the applications of nanomaterials in the field of flexible devices and beyond.