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. CNNbased
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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