臺灣博碩士論文加值系統

二氫葉酸還原酶(DHFR)抑制劑長期以來作為抗癌,抗瘧疾及抗菌的藥物的一種是很多學者研究的對象，早期的研究除了生物活性預測外，大多數的藥物分子設計利用定量構效關係(QSAR)模型來預測生物活性及分子設計，而近來多項的專業研究均引入了人工智慧作為演算取代傳統QSAR進行藥物分子設計。本研究建構一套人工智慧工作流程，其中包括從數據準備到分子模型構建和生物活性驗證。這個工作流程主要包含兩個網絡，描述如下。第一網絡是經由那些從ChEMBL數據庫中篩選出具有DHFR抑制活性的有效分子所訓練出的人工神經網絡。這個網絡可預測下一個網絡所建構小分子的抑制活性。第二網絡利用條件式生成對抗網絡建構具有所需要性質的候選分子的結構。最後所獲得的分子再經由分子對接的計算驗證其在DHFR活性區內對接模式與接合親和力大小。最終我們成功地篩選出三個新分子具有與現今臨床用藥相當的抑制活性。藉由本研究，我們提出新方法利用人工智慧有效地設計出新導化合物。

Used as anti-cancer, anti-malarial and antibacterial drugs, dihydrofolate reductase (DHFR) inhibitors have been one of the popular research objects for decades. In addition to the prediction of biological activity, quantitative structure–activity relationship (QSAR) are employed as a tool for molecular drug design. Recently artificial intelligence (AI) has been introduced in numerous professional researches for molecular drug design and biological activity prediction. In this study, we construct a AI workflow of de novo drug design for DHFR inhibitors data preparation to molecular model building and bio-activity validation. This workflow mainly includes two networks, as described in the following. The first one is the artificial neural network trained by the molecules selected from the ChEMBL database with experimental DHFR inhibitions to evaluate the bio-activity of the designed molecular structure constructed from the second network. The other network utilizes conditional generative and adversarial network (cGAN) to generate the candidate molecules with the desired properties. Finally, the obtained candidate molecules are subject to a molecular docking process for verifying their binding patterns and affinity strengths inside the active site of DHFR. In the end, we have successfully identified three new candidate molecules with the DHFR inhibition to comparable to those currently used in clinic. Through this study, we present a new method to effectively design new lead compounds through AI approach.

摘要i
Abstractii
目次iii
表目次vi
圖目次vii
第一章 緒論1
1.1. 葉酸(Folate) 1
1.2. 二氫葉酸還原酶(Dihydrofolate reductase，簡稱為DHFR) 1
1.2.1. DHFR與NADPH作用位點2
1.2.2. DHFR與葉酸結合位點3
1.3. 二氫還原酶抑制劑3
1.3.1. Aminopterin (4-aminopteroic acid) 3
1.3.2. Methotrexate (MTX) 4
1.3.3. MTX及DHF與DHFR之作用7
1.4. 葉酸作用機制7
1.4.1. THF甲基加成反應8
1.4.2. 5,10-亞甲基四氫葉酸去甲基化(dTMP合成) 9
第二章 實驗原理及方法10
2.1. 小型藥物AI設計的模式10
2.2. ChEMBL Database11
2.3. ECFP Fingerprint11
2.3.1.表達方式12
2.3.1.1. 整數標識符列表12
2.3.1.2. 定長位串13
2.3.2. 指紋生成14
2.3.2.1原子標識符的初始分配15
2.3.2.2. 標識符的迭代更新15
2.3.2.3. 重複刪除15
2.4 Torch Neural Network(TNN) 16
2.4.1. PyTorch16
2.4.2.QSAR模型建構17
2.5. Simplified molecular input line entry specification(簡稱為SMILES) 18
2.6. 加密交換(Token Swap) 19
2.7. Generative Adversarial Network20
2.7.1. Learning Discriminator21
2.7.2. Learning Generator22
2.7.3. Conditional Generative Adversarial Network (C-GAN)22
2.8.分子篩選與作用力分析23
2.8.1. AutoDock24
2.8.2. iGemdock24
第三章 結果與討論26
3.1. ECFP生成之QSAR模型26
3.2. cGAN生成結果27
3.3.Docking篩選28
3.3.1. Docking作用力初步預測29
3.3.2. Docking作用力綜合判定30
3.3.3. 新結構小分子與DHFR作用力之比較31
3.4. 經驗法則設計小分子化合物與受體對接結果32
3.4.1.類ID45小分子設計結果32
3.4.2.類ID54小分子設計結果33
第四章 結論35
參考書目36
附錄40
生成對抗產生之新分子40

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