牙周炎是一種由細菌、病毒和真核生物引發的慢性口腔炎症性疾病，是全球所周知的普遍疾病。儘管有有效的牙周炎治療方法，但其管理也存在一些缺點，包括治療選擇有限、復發風險和高昂的治療費用。我們的目標是在臨床試驗之前，為牙周炎開發一種更高效、系統化的藥物設計。我們通過系統生物學和深度學習方法，致力於牙周炎治療的系統性藥物發現和設計。首先，我們應用大型數據庫挖掘來建立候選的全基因組遺傳和表觀遺傳網絡（GWGEN），其中包括牙周炎和健康對照的蛋白質相互作用網絡（PPIN）和基因調控網絡（GRN）。接下來，基於不健康和健康的微陣列數據，我們應用系統識別和系統階數檢測方法來去除候選GWGEN中的假陽性結果，從而分別獲得牙周炎和健康對照的真實GWGEN。獲得真實GWGEN後，我們通過主網絡投影（PNP）方法選出核心GWGEN。最後，參考KEGG pathways，我們建立了牙周炎和健康對照的核心信號通路。我們通過比較它們的核心信號通路來研究牙周炎的致病機制。通過檢查牙周炎的下游核心信號通路和相應的細胞功能異常，我們選擇FOS、TSC2、FOXO1和NF-κB 作為生物標記物。接著，使用基於深度神經網絡的藥物-靶點相互作用（DTI）模型，該模型通過現有的藥物-靶點相互作用數據庫進行訓練，以預測針對重要生物標記物的候選分子藥物。最後，我們根據藥物設計規範，篩選出brucine、disulfiram、verapamil和PK-11195組合成多分子藥物，針對這些重要生物標記物進行靶向治療。總結來說，我們通過運用系統生物學方法研究了牙周炎的致病機制，並通過基於DNN的DTI模型的預測和藥物設計規範徹底地開發了牙周炎治療的治療選項。

Periodontitis, a chronic inflammatory oral condition triggered by bacteria, archaea, viruses, and eukaryotic organisms, is a well-known and widespread disease around the world. While there are effective treatments for periodontitis, there are also several shortcomings associated with its management, including limited treatment options, the risk of recurrence, and the high cost of treatment. Our goal is to develop a more efficient, systematic drug design for periodontitis before clinical trials. We work on systems drug discovery and design for periodontitis treatment via systems biology and deep learning methods. We first applied big database mining to build a candidate genome-wide genetic and epigenetic network (GWGEN), which includes a protein-protein interaction network (PPIN) and a gene regulatory network (GRN) for periodontitis and healthy control. Next, based on the unhealthy and healthy microarray data, we applied system identification and system order detection methods to remove false positives in candidate GWGENs to obtain real GWGENs for periodontitis and healthy control, respectively. After the real GWGENs were obtained, we picked out the core GWGENs based on how significant the proteins and genes were via the principal network projection (PNP) method. Finally, referring to the annotation of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we built up the core signaling pathways of periodontitis and healthy control. Consequently, we investigated the pathogenic mechanism of periodontitis by comparing their core signaling pathways. By checking up on the downstream core signaling pathway and the corresponding cellular dysfunctions of periodontitis, we identified the fos proto-oncogene, AP-1 Transcription Factor Subunit (FOS), TSC Complex Subunit 2 (TSC2), Forkhead Box O1 (FOXO1), and nuclear factor kappa-light chain enhancer of activated B cells (NF-κB) as significant biomarkers on which we could find candidate molecular drugs to target. To achieve our ultimate goal of designing a combination of molecular drugs for periodontitis treatment, a deep neural network (DNN)-based drug-target interaction (DTI) model was employed. The model is trained with the existing drug-target interaction databases for the prediction of candidate molecular drugs for significant biomarkers. Finally, we filter out brucine, disulfiram, verapamil, and PK-11195 as potential molecular drugs to be combined as a multiple-molecular drug to target the significant biomarkers based on drug design specifications, i.e., adequate drug regulation ability, high sensitivity, and low toxicity. In conclusion, we investigated the pathogenic mechanism of periodontitis by leveraging systems biology methods and thoroughly developed a therapeutic option for periodontitis treatment via the prediction of a DNN-based DTI model and drug design specifications.

1. Introduction………………………………………………………………………………………………………1
2. Results………………………………………………………………………………………………………………4
2.1. Overview of Systems Biology Method for Pathology Mechanism and Systematic Drug Discovery and Design for Periodontitis Treatment…………………4
2.2. Comparing Core Signaling Pathways of Periodontitis and Healthy Control to Identify Biomarkers of Pathological Mechanism of Periodontitis………………………9
2.3. Systematic Drug Discovery Based on Deep Neural Network-Based Drug-Target Interaction Model for Periodontitis Treatment…………………………………………………11
3. Discussion………………………………………………………………………………………………………..17
4. Materials and Methods…………………………………………………………………………………….19
4.1. Systems Biology Methods and Systematic Drug Design for Periodontitis Treatment: An Overview…………………………………………………………………………………..19
4.2. Data Preprocessing and Big Data Mining for the Construction of Candidate GWGEN…………………………………………………………………………………………………………….204.3. Construction of the Stochastic System Model to Obtain Real GWGEN of Periodontitis by System Identification Method………………………………………………….21
4.4. Constructing Real GWGENs of Periodontitis and Healthy Control by System Identification and System Order Detection Methods…………………………………………23
4.5. Extraction of the Core GWGEN from Real GWGEN for Core Signaling Pathways via Principal Network Projection (PNP) Method…………………………………28
4.6. Systematic Drug Discovery for Periodontitis Treatment via DNN-Based DTI Model Prediction and Drug Design Specifications……………………………………………..31
5. Conclusion………………………………………………………………………………………………………..34
6. References………………………………………………………………………………………………………..35

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