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研究生:張立杰
研究生(外文):Li-Jie Zhang
論文名稱:利用具有相互作用依賴性的條件變分自動編碼器建構μ 阿片受體偏向激動劑分子設計的生成模型
論文名稱(外文):A generative model for molecular design of biased agonists for μ opioid receptor through interaction-dependent conditional variational autoencoder
指導教授:李豐穎
指導教授(外文):Feng-Yin Li
口試委員:蔡柏宇胡景瀚
口試委員(外文):Po-Yu TsaiChing-Han Hu
口試日期:2023-07-24
學位類別:碩士
校院名稱:國立中興大學
系所名稱:化學系所
學門:自然科學學門
學類:化學學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:78
中文關鍵詞:鴉片偏向激活劑交互作用力人工智慧條件變分自編碼器
外文關鍵詞:OpioidBiased agonistInteractionAICVAE
相關次數:
  • 被引用被引用:0
  • 點閱點閱:27
  • 評分評分:
  • 下載下載:4
  • 收藏至我的研究室書目清單書目收藏:0
µ鴉片受體的配體具有極高的鎮痛效果,但同時亦具有嚴重的負作用。基於偏置配體之可能性,可藉由分子設計開發新奇配體產生具鎮痛效果且能降低副作用的研究近年來逐漸受到重視。然由於偏置配體與受體的作用機制尚未清楚,相關研究進展相當有限,目前需要開發更多類似性質之配體以利於研究。本研究嘗試使用以配體-受體作用力值為建構條件的條件變分自編碼器(conditional variational autoencoder, CVAE)作為開發新偏置配體之工具。透過我們設計的CVAE,成功生成多個新奇配體並建構功能鑑別器以預測該生成分子之生理作用參數。
Ligands of µ-opioid receptors have extremely high analgesic effects, but they also have serious side effects. Based on the possibility of biased ligands, it gradually attracts attention recently to design novel ligands that can produce analgesic effects and reduce side effects. However, because the detailed mechanism of action between biased ligands and receptors remains unclear, relevant research progress is quite limited, and therefore, more ligands with this type of properties need to be explored to facilitate the research. This study attempts to use the conditional variational autoencoder (CVAE) as a tool to design novel biased ligands. Through our designed CVAE, numerous novel biased drug leads were successfully generated and a functional discriminator was constructed to predict their physiological activity parameters.
摘要 i
Abstract ii
目次 iii
表目次 v
圖目次 vi
第一章 導論 1
1.1 藥物設計 1
1.1.1 過去 1
1.1.2 現今 2
1.1.3 人工智慧(Artificial Intelligence,簡稱AI)的輔助 2
1.1.4 AI對分子的從頭設計 2
1.2 鴉片藥物 3
1.2.1 歷史 3
1.2.2 種類 5
1.2.3 鴉片受體之結構 6
1.2.4 鴉片受體之啟動 6
1.2.5 神經訊號傳遞與突觸抑制 8
1.2.6 偏置激活劑 9
1.2.7 配體衡量 10
1.2.8 A類GPCR共通啟動細節 11
1.3 類神經網路(Neural Network) 13
1.3.1 歷史 13
1.3.2 人工神經網路(Artificial Neural Network,縮寫為ANN) 14
1.3.3 激活函數(Activation Function) 15
1.3.4 損失函數(Loss Function) 16
1.3.5 反向傳播(Backpropagation) 17
1.3.6 優化器(Optimizer) 20
1.3.7 卷積神經網路(Convolutional Neural Networks,簡稱為CNN) 23
1.3.8 循環神經網路(Recurrent neural network,簡稱為RNN) 24
1.3.9 變分自編碼器(Variational Autoencoder,簡稱為VAE) 26
1.3.10 AI的可解釋性 28
1.4 分子表示 29
1.4.1 簡化分子線性輸入規範(Simplified Molecular Input Line Entry Specification,簡稱為SMILES) 29
1.4.2 熱編碼(One Hot Encoding) 30
1.4.3 分子指紋(Molecular Fingerprints) 31
研究動機 32
第二章 方法 33
2.1 資料集 34
2.2 對接前處理 35
2.3 分子對接 36
2.4 作用力分析 37
2.5 預測器訓練 38
2.6 生成器前置 40
2.7 生成器 41
第三章 結果與討論 44
3.1 預測器分析 44
3.1.1 pIC50預測器與pEC50預測器 46
3.1.2 Bias factor預測器 47
3.2 生成器分析 52
3.2.1 訓練過程中,損失的變化 52
3.2.2 每10週期之模型生成之分子 53
3.2.3 有效性、獨特性、新穎性 55
3.2.4 條件約束力 56
3.2.5 潛在空間之主成分分析 58
3.2.6 生成分子的分析 59
3.2.7 影響Bias factor之可原因 61
第四章 結論 62
參考書目 63
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