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- W4385993404 abstract "As a branch of machine learning techniques, deep learning (DL) has achieved state-of-the-art performances in a wide range of applications, e.g., speech processing, computer vision, and natural language processing. In this chapter, we first briefly introduce the basic concepts of the most popular deep neural network, convolutional neural network, recurrent neural network, and graph neural network. Then, the key DL techniques and recent applications of DL methods in the quantitative structure–activity relationship (QSAR) researches of chemical toxicity prediction and risk assessment are systematically reviewed. Lastly, the limitations, challenges, and trends of DL for QSAR researches are discussed." @default.
- W4385993404 created "2023-08-19" @default.
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- W4385993404 date "2023-01-01" @default.
- W4385993404 modified "2023-10-14" @default.
- W4385993404 title "Deep learning quantitative structure–activity relationship methods for chemical toxicity prediction and risk assessment" @default.
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- W4385993404 doi "https://doi.org/10.1016/b978-0-443-15339-6.00019-9" @default.
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