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Convolutional Neural Network-Based Virtual Screening

Author(s):

Wenying Shan, Xuanyi Li, Hequan Yao and Kejiang Lin*   Pages 1 - 14 ( 14 )

Abstract:


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 been constantly 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, structure-based virtual screening, scoring function.

Affiliation:

Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing



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