Docking-based Virtual Screening with Multi-Task Learning
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Fan Wang
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Haifeng Wang
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Hua Wu
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Xianbin Ye
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Xiaomin Fang
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Zijing Liu
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Baidu Inc Shenzhen China
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Jinan University Guangzhou China
As the number of compounds in chemical libraries available to screening grows rapidly, machine learning approaches play an important role in docking-based virtual screening. Current works for docking barely consider using the docking data of other targets from previous screens.This model focusses on the application of Multi-task learning(MTL) in docking-based virtual screening. Machine learning shows great potential in virtual screening for drug discovery. Current efforts on accelerating docking-based virtual screening do not consider using existing data of other previously developed targets. To make use of the knowledge of the other targets and take advantage of the existing data this model apply multi-task learning to the problem of docking-based virtual screening. With two large docking datasets, the results of extensive experiments show that multi-task learning can achieve better performances on docking score prediction. By learning knowledge across multiple targets, the model trained by multi-task learning shows a better ability to adapt to a new target.
Input variables : Chemical compounds represented in form Molecular graphs converted using RDKit
Output Variables : Predict docking score
Metrics to Monitor
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Statistical
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Somers D |
Accuracy |
Precision and Recall |
Confusion Matrix |
F1 Score |
Roc and Auc |
Prevalence |
Detection Rate |
Balanced Accuracy |
Cohen's Kappa |
Concordance |
Gini Coefficent |
KS Statistic |
Youden's J Index
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Infrastructure
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Log Bytes |
Logging/User/IAMPolicy |
Logging/User/VPN |
CPU Utilization |
Memory Usage |
Error Count |
Prediction Count |
Prediction Latencies |
Private Endpoint Prediction Latencies |
Private Endpoint Response Count
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Visit Model :
arxiv.org