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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曾宇鳳(Yufeng Jane Tseng) | |
| dc.contributor.author | Yu-Hao Ni | en |
| dc.contributor.author | 倪宇澔 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:11:58Z | - |
| dc.date.copyright | 2022-09-27 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-26 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84449 | - |
| dc.description.abstract | 細胞色素CYP450的解毒能力對於調節我們體內中的藥物或有害化合物起著至關重要的作用。 其中，CYP1A2、CYP2C9、CYP2C19、CYP2D6、CYP2E1 和 CYP3A4 是負責 90% 以上藥物代謝的主要酵素。CYP450對於我們體內藥物的生物利用度有著顯著影響，而這也和藥理功效相關。因此，預測CYP450 介導的小分子代謝能加速配體的篩選過程並且對於藥物開發的早期階段至關重要。所有相關研究中用於預測CYP450介導代謝的QSAR模型所使用的數據集仍然不足且雜亂。此外，由於數據集中資料的有限，這些模型的表現也還不夠好或不適用。因此，本研究中我們系統性的評估了多種機器學習和深度學習方法，並且使用了多種優化方式，最後為六種重要的 CYP450 酵素構建了一組穩健的預測模型。此外，我們從所有可用的資料來源中蒐集了所有和CYP450介導代謝作用的化合物，經過一系列的前處理和人工驗證方法，最後提供一組最全面、高效且最新的化合物數據集。結果表明，經過我們所整理的數據集訓練的預測模型和其他研究的訓練集資料相比表現得更好，證明我們所使用的清理及驗證方式是有效的。另外，經過對各種演算方法的系統評估，圖形卷積網絡（GCN）所構建的預測模型性能最高，MCCs落在0.50（CYP2C19）和0.72（CYP1A2）之間。這些成果有助於其他CYP450交互作用的預測模型開發，並能支持藥物開發過程中的化合物篩選。 | zh_TW |
| dc.description.abstract | The detoxification ability of cytochrome P450(CYP450) enzymes plays an essential role in regulating the existence of drugs or harmful compounds in our bodies. CYP1A2, CYP2C9, 2C19, CYP2D6, CYP2E1, and CYP3A4 are the main enzymes in charge of over 90 percent of drug metabolism. Consequently, they significantly affect the bioavailability of drugs in our body, which are related to pharmacological efficacy. Therefore, predicting the property of small molecules related to CYP450-mediated metabolism is essential for the early stage of drug discovery by accelerating the ligand screening process. Datasets used in QSAR models for CYP450 substrate prediction from all related studies are still insufficient and inconsistent. Furthermore, the performance of these models is also insufficient or non-applicable due to the limited dataset. In this case, we construct a set of robust predicting models for the six essential CYP450 enzymes by evaluating various machine learning and deep learning approaches with practical optimization techniques. In addition, we presented the most comprehensive and high-quality datasets of compounds with CYP450 enzyme interaction from all available resources. The high quality of the dataset proceeds through various artificial curation means. The results show that our highly curated dataset possesses competitive capacity on model training for CYP450 substrate prediction. After a systematic evaluation of various approaches, predicting models constructed by Graph Convolution Network (GCN) algorithm reached the highest performance, with Matthews correlation coefficients (MCCs) between 0.50(CYP2C19) and 0.72(CYP1A2). We hope these achievements are helpful for other in silico approaches on CYP450 mediated metabolism and support the compound screening section during drug development. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:11:58Z (GMT). No. of bitstreams: 1 U0001-2109202210562400.pdf: 3949814 bytes, checksum: 2aacbc841c3175d35e3881a3335b5d16 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 致謝 i 中文摘要 ii Contents iii Abstract vi List of Figures vii List of Tables viii Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Algorithms for QSAR model construction 2 1.3 Reviews of the related studies 5 1.4 Thesis Motivation and Novelty in this Study 12 Chapter 2 Materials and Methods 14 2.1 Dataset preparation 14 2.1.1 Data collection 14 2.1.2 Data curation 16 2.2 QSAR model construction and feature extraction 19 2.2.1 Construction of traditional machine learning models 19 2.2.2 Construction of deep learning models 20 2.3 Model Optimization 21 2.3.1 Data augmentation through SMILES enumeration 21 2.3.2 Data splitting for Cross-validation and Hyperparameter Optimization 21 2.3.3 Algorithm for hyperparameter tuning 23 2.3.4 Epoch optimization for GCN models 23 2.4 Model evaluation and Comparison 24 2.4.1 Performance metrics 24 2.4.2 External testing sets for model evaluation 25 Chapter 3 Result 27 3.1 GCN-based classifiers for CYP450 metabolism 27 3.1.1 Evaluation of our curated dataset 29 3.2 Comparisons of other model algorithms on prediction of CYP-mediated metabolism 30 3.2.1 Comparison of the neural network-based classifiers 30 3.2.2 Comparison of the traditional machine learning classifiers 32 3.3 Comparison of our best performing models to the CYPstrate models 35 Chapter 4 Discussion 38 4.1 Comprehensively-Curated Dataset 38 4.2 Advantages of the GCN approaches to CYP450 modeling 39 4.3 Compare with related work 39 4.4 Capacity of CYP450 interaction prediction on steroids 41 4.5 Limitations of this study 42 Chapter 5 Conclusion and Future Work 44 5.1 Conclusion 44 5.2 Future Work 45 Reference 46 | |
| dc.language.iso | en | |
| dc.subject | QSAR模型 | zh_TW |
| dc.subject | 細胞色素P450介導代謝 | zh_TW |
| dc.subject | 全面性數據集 | zh_TW |
| dc.subject | 圖形卷積網絡 | zh_TW |
| dc.subject | 機器學習的系統評估 | zh_TW |
| dc.subject | Systematic evaluation of ML methods | en |
| dc.subject | CYP450-mediated | en |
| dc.subject | Comprehensively-Curated Dataset | en |
| dc.subject | QSAR model | en |
| dc.subject | Graph Convolution Network (GCN) | en |
| dc.title | 用於預測細胞色素P450介導代謝的全面性數據集：機器學習和深度學習的系統評估 | zh_TW |
| dc.title | A Comprehensively-Curated Dataset for Prediction of Cytochrome P450 Isoforms Mediated Metabolism: A Systematic Evaluation of Machine Learning and Deep Learning Methods | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蘇柏翰(Bo-Han Su),杜羿樞(Yi-Shu Tu) | |
| dc.subject.keyword | 細胞色素P450介導代謝,全面性數據集,QSAR模型,圖形卷積網絡,機器學習的系統評估, | zh_TW |
| dc.subject.keyword | CYP450-mediated,Comprehensively-Curated Dataset,QSAR model,Graph Convolution Network (GCN),Systematic evaluation of ML methods, | en |
| dc.relation.page | 56 | |
| dc.identifier.doi | 10.6342/NTU202203704 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-09-26 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-09-27 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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