自 2019 年 12 月下旬報導以來，2019 年冠狀病毒病大流行已經奪走了許多人的生命。但是，仍然沒有證明對它有效的藥物。本研究通過大數據挖掘構建候選宿主-病原體交互全基因組遺傳和表觀遺傳網絡（HPI-GWGEN）。通過RNA-seq時間剖析資料，應用還原工程去除候選HPI-GWGEN中的假陽性來研究SARS-CoV-2感染的發病機制。隨後，通過主網絡投影方法和KEGG信號通路的註釋，我們確定了重要的生物標誌物作為藥物靶標，以破壞SARS-CoV-2複製的有利環境或增強宿主細胞對它的防禦。為了發現靶向重要生物標誌物（作為藥物靶標）的多分子藥物，訓練了基於深度神經網路架構的藥物標靶作用模型來預測這些藥物靶標的候選分子藥物。通過基於深度神經網路架構的藥物標靶作用模型，我們預測了針對重要生物標誌物（藥物靶標）的候選藥物。在篩選出符合藥物設計規範的候選藥物後，我們最終提出了波舒替尼、厄洛替尼和17-β-雌二醇作為治療SARS-CoV-2感染早期病毒載量快速放大期的多分子藥物以及厄洛替尼、17-β-雌二醇和舍曲林作為治療SARS-CoV-2感染病毒飽和期的多分子藥物。

The coronavirus disease 2019 (COVID-19) pandemic has claimed lots of lives since it has been reported in late December 2019. However, there is still no drug proven effective against it. In this study, the candidate host-pathogen-interactive (HPI) genome-wide genetic and epigenetic network (HPI-GWGEN) was constructed by big data mining. The reverse engineering method was applied to investigate the pathogenesis of SARS-CoV-2 infection by pruning the false positives in candidate HPI-GWGEN through the HPI RNA-seq time-profile data. Subsequently, with the principal network projection method and the annotation of KEGG pathway, we identified the significant biomarkers as drug targets to destroy the favorable environment for the replication of SARS-CoV-2 or enhance the defense of host cell against it. To discover the multiple-molecule drugs to target the significant biomarkers (as drug targets), a DNN-based DTI model was trained to predict candidate molecular drugs for these drug targets. By DNN-based DTI model, we predicted the candidate drugs targeting the significant biomarkers (drug targets). After screening candidate drugs with drug design specifications, we finally proposed bosutinib, erlotinib, and 17-beta-estradiol as the multiple-molecule drug for the treatment of rapid viral amplification stage of SARS-CoV-2 infection and erlotinib, 17-beta-estradiol, and sertraline as the multiple-molecule drug for the treatment of viral saturation stage of SARS-CoV-2 infection.

摘要--------I
Abstract--------II
誌謝--------III
Contents--------IV
1. Introduction--------1
2. Methods and Materials--------4
2.1 Overview the Systematic Drug Discovery--------4
2.2 Construction of the Candidate HPI-GWGEN by Big Data Mining--------6
2.3 Reverse Engineering Method by the Dynamic Models and HPI RNA-seq Time-Profile Data--------6
2.3.1 HPI RNA-seq Time-Profile Data--------6
2.3.2 Dynamic Models for HPI-GWGEN--------7
2.3.3 System Identification and System Order Selection for HPI-GWGEN--------11
2.4 PNP Method to Extract the Core HPI-GWGEN from Network Matrix of Real HPI-GWGEN--------18
2.5 Systematic Discovery and Design of Multiple-Molecule Drug by Utilizing DNN-Based DTI Model with Drug Design Specifications--------19
2.5.1 Preprocess of Targets and Drugs Data--------21
2.5.2. Architecture of DNN-Based DTI Model--------22
2.5.3. Drug Design Specifications--------22
3. Results--------23
3.1 Core HPI Signaling Pathways during Amplification and Saturation Stages of SARS-CoV-2 Infection by the Systems Biology Method--------23
3.2 Investigation of Core Specific HPI Signaling Pathways and their Downstream Abnormal Cellular Functions during SARS-CoV-2 Infection--------29
3.2.1 Investigation of Specific Core HPI Signaling Pathways of Amplification Infectious Stage--------29
3.2.2 Investigation of Common Core HPI Signaling Pathways of Amplification and Saturation Infectious Stages--------31
3.2.3 Investigation of Specific Core HPI Signaling Pathways of Saturation Infectious Stage--------33
3.3 Multiple-Molecule Drug Discovery and Design by DNN-Based DTI Model with Drug Design Specifications--------34
3.3.1. Prediction Performance of DNN-Based DTI Model--------35
3.3.2 Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection--------37
4. Discussion--------40
5. Conclusion--------42
Reference--------43
Appendix A--------51

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