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Front Pharmacol. 2019 Aug 22;10:924. doi: 10.3389/fphar.2019.00924. eCollection 2019.

Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods.

Wang D1,2, Cui C1,2, Ding X1,2, Xiong Z3, Zheng M1, Luo X1, Jiang H1,3, Chen K1,3.

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Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
College of Pharmacy, University of Chinese Academy of Sciences, Beijing, China.
School of Life Science and Technology, ShanghaiTech University, Shanghai, China.


Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effectively construct TSSFs will be of great value to drug design and discovery. In this work, we proposed a deep learning-based model named DeepScore to achieve this goal. DeepScore adopted the form of PMF scoring function to calculate protein-ligand binding affinity. However, different from PMF scoring function, in DeepScore, the score for each protein-ligand atom pair was calculated using a feedforward neural network. Our model significantly outperformed Glide Gscore on validation data set DUD-E. The average ROC-AUC on 102 targets was 0.98. We also combined Gscore and DeepScore together using a consensus method and put forward a consensus model named DeepScoreCS. The comparison results showed that DeepScore outperformed other machine learning-based TSSFs building methods. Furthermore, we presented a strategy to visualize the prediction of DeepScore. All of these results clearly demonstrated that DeepScore would be a useful model in constructing TSSFs and represented a novel way incorporating deep learning and drug design.


DUD-E; deep learning; drug discovery; target-specific scoring function; virtual screening

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