乳腺癌抗性蛋白 (BCRP/ABCG2) 是ABC轉運蛋白家族的成員之一，總共有七個亞科: 從 ABCA到 ABCG。這些轉運蛋白可以將物質運送到細胞膜外。在這些轉運蛋白中，P-glycoprotein, BCRP, 和 multidrug resistance-associated protein 1與多藥耐藥相關。而且P-glycoprotein, BCRP與藥物在大腦中的滲透性中也起著重要的作用。因此，建立理論模型是必要的，並使用模型來預測這兩種轉運蛋白的抑制作用。此外，與P-glycoprotein 抑制的模型相比，BCRP抑制的已發表模型較少。本研究使用pharmacophore ensemble/support vector machine (PhE/SVM)方法建立了一個計算機預測模型，用來預測BCRP的混雜特徵。PhE/SVM模型在training set (r2 = 0.82, = 0.73, n = 22), 和test set (q2 = 0.75-0.89, n = 97), 和outlier set (q2 = 0.72-0.91, n = 16) 的表現是準確的，並且通過了所有的嚴格標準。此外，在HIV蛋白酶抑制劑的模擬測試中也是預測準確。因此，這種PhE/SVM 模型可用於預測BCRP抑制，該模型還可以評估BCRP配體的結構多樣性和不同方向。

Breast cancer resistance protein (BCRP/ABCG2) is a member of the ATP-binding cassette transporter family. This superfamily has seven subfamilies: ABCA to ABCG. These transporters can transport substances and xenobiotics to extracellular. Among these transporters, P-glycoprotein, BCRP, and multidrug resistance-associated protein 1 are associated with multidrug resistance. Both P-glycoprotein and BCRP also play major roles in the permeability of drugs in the brain. Therefore, building theoretical models is necessary and use models to predict the inhibition of both of these transporters. Furthermore, there are fewer published prediction models for BCRP inhibition than for P-glycoprotein inhibition. This study built an in silico predictive model using a pharmacophore ensemble/support vector machine (PhE/SVM) to evaluate the promiscuous characteristics of BCRP. The prediction performance of the PhE/SVM model in the training set (r2 = 0.82, = 0.73, n = 22), test set (q2 = 0.75-0.89, n = 97), and outlier set (q2 = 0.72-0.91, n = 16) were accurate and passed all stringent criteria. In addition, the mock test prediction with HIV protease inhibitors was also accurate. Consequently, this in silico PhE/SVM model can be used to predict BCRP inhibition. The model can also evaluate the structural diversity and different orientations of the BCRP ligand.

誌謝 ii
摘要 iii
Abstract iv
CONTENTS v
ABBREVIATIONS 1
Introduction 3
Method 6
Data collection 6
Pharmacophore development 6
SVM calculations 7
Validations 8
Results 11
PhE 11
PhE/SVM 13
Robustness evaluation 14
Predictive validations 15
Mock test 16
Discussion 17
Conclusion 20
Table S1 These molecules are collected in this study, the molecule detail are names, SMILES strings, observed and predicted pIC50 values in Hypo A, Hypo B, Hypo C, PhE/SVM, data partitions, and references 46
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