I am a PhD student with the topic "Computational Drug repurposing". I am applying for a scholarship and for that I have to write an essay.  Based on the following goals which I will state, can you write me some paragraphs about which will be the scientific innovation of my dissertation for that scholarship essay?

Here is the goals
"Step 1: Design of the Method
In this step, we will focus on designing a robust, scalable, and efficient computational machine learning pipeline for drug repurposing. We will explore the state-of-the-art techniques in machine learning, specifically deep learning approaches, and tailor them to address our specific needs in drug repurposing. Our core methodology will be the development of a graph embedding function that can preprocess various types of graphs (e.g., chemical structures, protein-protein interaction networks, disease-drug associations, etc.) and convert them into a suitable format for serving as input to neural networks. The design phase will involve reviewing relevant literature in graph representation learning, comparing different graph embedding techniques, and determining the most suitable architecture of neural networks (e.g., Graph Convolutional Networks, Graph Attention Networks) to be employed in the pipeline.

Step 2: Development of the Method
Once the pipeline design is finalized, the focus will shift towards the development of the method. This phase will involve the implementation of the chosen machine learning models and graph embedding functions, along with the creation of a user-friendly interface for researchers to interact with the pipeline. We will also need to preprocess and curate a large-scale dataset containing information on drugs, diseases, and their molecular interactions. Our goal will be to create a high-quality, well-annotated dataset that can be used for training and testing the machine learning models. Finally, we will develop the necessary data processing and input/output handling modules that will enable the seamless integration of different components of the computational pipeline.

Step 3: Application of the Method in Certain Diseases
In this phase, we will apply the developed computational pipeline to identify potential drug repurposing candidates for specific diseases of interest. We will first train our machine learning models using the curated dataset and fine-tune the model parameters to optimize the performance. Next, we will input disease-specific graphs into the pipeline and use our trained models to predict novel drug-disease associations or targetable pathways. By doing so, we aim to generate a list of candidate drugs that can potentially be repurposed for treating the selected diseases. These candidates can then be subjected to further experimental validation.

Step 4: Evaluation of the Method
The final step of our machine learning pipeline is the evaluation of its performance in predicting novel drug repurposing candidates. This will involve comparing the predicted drug-disease associations with known associations from the literature or experimental data. We will use various performance metrics, such as precision, recall, F1-score, and area under the ROC curve (AUROC), to quantitatively assess the prediction accuracy of our pipeline. Moreover, we may conduct case studies focusing on specific diseases to demonstrate the relevance and impact of the predicted repurposing candidates on the disease biology. Through these evaluations, we aim to demonstrate the effectiveness of our pipeline in discovering potential drug repurposing opportunities and providing valuable insights for further experimental investigations.
"