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Convolutional Neural Network-based Virtual Screening
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DOI：10.2174/0929867327666200526142958.png)
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Abstract
En 中文
Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different scoring functions tend to have conflicting results. Recently, neural networks, especially convolutional neural networks, have constantly been penetrating drug design and most CNN-based virtual screening methods are superior to traditional docking methods, such as Dock and AutoDock. CNN-based virtual screening is expected to improve the previous model of overreliance on computational chemical screening. Utilizing the powerful learning ability of neural networks provides us with a new method for evaluating compounds. We review the latest progress of CNN-based virtual screening and propose prospects.
Keywords:
Deep learning
CNN-based virtual screening
scoring function
Dock
AutoDock
chemical screening
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