藥物研發的過程中，尚未被發現的藥物與標的之交互作用扮演著關鍵的角色。近來，電腦(對接模擬與基於機器學習)技術興起，以取代高成本、高耗時的生物實驗。在機器學習的技術裡，許多基於網路的方法被提出，例如：受限玻爾茲曼機、二分區域模型、基於網路之推演、加權資料方法和進階區域式藥物與蛋白質交互作用預測之技術。在此研究中，我們擴展受限玻爾茲曼機，藉由整合重要特徵，例如：藥物與藥物之間的相似度和標的與標的之間的相似度。此外，我們將尚未被考慮的藥物與藥物之間關聯性，合併至原始的受限玻爾茲曼機。最後，我們提出了相似性增強混合式條件受限玻爾茲曼機，其是受到推薦系統界中的內容增強式受限玻爾茲曼機所啟發。我們的實驗結果顯示出我們的方法優於先前由Wang與Zeng所提出的玻爾茲曼機。尚未被發現的藥物與標的之交互作用在藥物研發的過程中扮演著關鍵的角色。近來，電腦輔助(對接模擬與基於機器學習的方法)技術興起，以取代高成本、高耗時的生物實驗。在機器學習的領域裡，有許多基於網路的方法被提出，例如：受限玻爾茲曼機、二分區域模型、基於網路之推演、加權資料方法和進階區域式藥物與蛋白質交互作用預測之技術。在此研究中，我們透過整合重要特徵，例如：藥物與藥物之間的相似度和標的與標的之間的相似度，來擴展受限玻爾茲曼機。此外，我們將尚未被考慮的藥物與藥物之間的關聯性，合併至原始的受限玻爾茲曼機。最後，受到推薦系統界中的內容增強式受限玻爾茲曼機所啟發，我們提出了相似性增強混合式條件受限玻爾茲曼機。
我們的實驗結果顯示出我們的方法優於先前由Wang與Zeng所提出的玻爾茲曼機。

Uncovering drug-target interactions plays a key role in the drug development
process. Recently, in silico (docking simulation and machine learningbased)
techniques have emerged as an alternative to costly and time consuming
biochemical experiments. In machine learning-based techniques, many
network-based approaches have been proposed such as Restricted Boltzmann
Machine (RBM), Bipartite Local Models (BLM), Network Based Inference
(NII), Weighted profile method and Advanced Local Drug-Target
Interaction Prediction Technique (ALADIN). In this research, we extend the
RBM by integrating important features such as drug-drug and target-target
similarity. In addition, we incorporate the correlations between drugs that
have not been taken into account in the original RBM. Finally, we propose
a Similarity-Boosted Hybrid Conditional RBM (SB H-CRBM) which is inspired
by the Content-Boosted Restricted Boltzmann Machine(CB-RBM) [1] from the recommendation systems community.
Our experimental results show that our method performs better than the
RBM was previously proposed by Wang and Zeng.

Contents
Abstract 2
Acknowledgement 4
List of Tables 7
List of Figures 8
1 Introduction 1
2 RelatedWork 5
3 Methodology 8
3.1 RBM for drug-target interaction prediction . . . . . . . . . . 8
3.2 Conditional RBM for drug-target prediction . . . . . . . . . 13
3.3 Hybrid RBM for drug-target prediction . . . . . . . . . . . 14
3.4 Weighted Profile Method . . . . . . . . . . . . . . . . . . . 17
3.5 Similarity-Boosted CRBM . . . . . . . . . . . . . . . . . . 18
3.6 Similarity-Boosted Hybrid CRBM (SB H-CRBM) . . . . . . 19
3.7 Implementation . . . . . . . . . . . . . . . . . . . . . . . . 23
4 Experimental Evaluation 25
4.1 Experimental Settings . . . . . . . . . . . . . . . . . . . . . 25
4.1.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . 25
4.1.2 Evaluation Protocol and Metrics . . . . . . . . . . . 28
4.1.3 Baseline . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Experimental Results and Discussion . . . . . . . . . . . . . 29
4.2.1 Distinction between direct and indirect interactions . 30
4.2.2 No distinction between direct and indirect interactions 34
5 Conclusion and Future Work 39
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 40
References 41
List of Tables
4.1 Statistics of MATADOR dataset . . . . . . . . . . . . . . . 27
4.2 Statistics of MATADOR dataset after cross reference . . . . 28
List of Figures
2.1 RBM for drug-target interaction . . . . . . . . . . . . . . . 7
3.1 Drug-Target RBM for drug-target interaction prediction . . . 13
3.2 Drug-Drug chemical similarity matrix . . . . . . . . . . . . 15
3.3 Target-Target genomic similarity matrix . . . . . . . . . . . 16
3.4 Drug-Target interaction matrix . . . . . . . . . . . . . . . . 16
4.1 Comparison of AUROC for Direct interaction prediction. . . 31
4.2 Comparison of AUPR for Direct interaction prediction. . . . 32
4.3 Comparison of AUROC for Indirect interaction prediction. . 33
4.4 Comparison of AUPR for Indirect interaction prediction. . . 34
4.5 Comparison of AUROC for Direct interaction prediction (no
distinction). . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.6 Comparison of AUPR for Direct interaction prediction (no
distinction). . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.7 Comparison of AUROC for Indirect interaction prediction
(no distinction). . . . . . . . . . . . . . . . . . . . . . . . . 36
4.8 Comparison of AUPR for Indirect interaction prediction (no
distinction). . . . . . . . . . . . . . . . . . . . . . . . . . . 37

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