Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods - Wang, Dingyan; Cui, Chen; Ding, Xiaoyu; Xiong, Zhaoping; Zheng, Mingyue; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian (Frontiers in Pharmacology, 2019)
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 Gscore on validation dataset 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.