Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods.
Front Pharmacol, 2019;10:924.
Wang D[1, 2], Cui C[1, 2], Ding X[1, 2], Xiong Z[3], Zheng M[1], Luo X[1], Jiang H[1, 3], Chen K[1, 3]
Affiliations
PMID: 31507420DOI: 10.3389/fphar.2019.00924
Abstract
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.
Keywords: DUD-E; deep learning; drug discovery; target-specific scoring function; virtual screening
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