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- W3208111806 abstract "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. Experimental results demonstrated that the proposed predictive tools achieved the desired performance in terms of AUC." @default.
- W3208111806 created "2021-11-08" @default.
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- W3208111806 date "2021-11-02" @default.
- W3208111806 modified "2023-09-27" @default.
- W3208111806 title "InflamNat: Web-Based Database and Predictor of Anti-Inflammatory Natural Products" @default.
- W3208111806 doi "https://doi.org/10.21203/rs.3.rs-1030714/v1" @default.
- W3208111806 hasPublicationYear "2021" @default.
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