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Deep Learning For Molecular Property Prediction, Hehuan Ma
Deep Learning For Molecular Property Prediction, Hehuan Ma
Computer Science and Engineering Dissertations
Drug discovery has always been a crucial task for society, and molecular property prediction is one of the fundamental problem. It is responsible for identifying the target properties or severe side-effects, so that certain molecules can be selected as the candidates of drugs. Traditional methods usually conduct a series of biochemical experiments to test the molecular properties, which may take up to decades. Nowadays, this process can be facilitated due to the rapid growth of deep learning methods. I present my work toward solving this critical problem by utilizing deep learning techniques. My research study can be summarized in three …
Adaptive Graph Convolutional Neural Network And Its Biomedical Applications, Ruoyu Li
Adaptive Graph Convolutional Neural Network And Its Biomedical Applications, Ruoyu Li
Computer Science and Engineering Dissertations
As the rise of graph neural networks, many deep learning frameworks have been extended to graph-structured data. The research in many diverse regimes have been tremendously reshaped, especially in areas like medical image understanding. When input data reach the scale of whole slides images (WSIs), the modeling becomes more challenging and we have to balance the trade-off between performance and efficiency. Furthermore, the theory of existing graph convolution has its own constraints which prevent learning robust graph representation on data that has diverse topological structure and are infeasible for graph sampling or coarsening. To tackle the problems we introduced a …
Comprehensive Study Of Generative Methods On Drug Discovery, Siyu Xiu
Comprehensive Study Of Generative Methods On Drug Discovery, Siyu Xiu
Computer Science and Engineering Theses
Observing the recent success of the deep learning (DL) technology in multiple life-changing application areas, e.g., autonomous driving, image/video search and discovery, natural language processing, etc., many new opportunities have presented themselves. One of the biggest ones lies in applying DL in accelerating the drug discovery, where millions of human lives could potentially be saved. However, applying DL into the drug discovery task turns out to be non-trivial. The most successful DL methods take fix-sized tensors/matrices, e.g., images, or sequences of tokens, e.g., sentences with variant numbers of words, as their inputs. However, none of these registers with the inputs …
Towards End-To-End Semi-Supervised Deep Learning For Drug Discovery, Xiaoyu Zhang
Towards End-To-End Semi-Supervised Deep Learning For Drug Discovery, Xiaoyu Zhang
Computer Science and Engineering Theses
Observing the recent progress in Deep Learning, the employment of AI is surging to accelerate drug discovery and cut R&D costs in the last few years. However, the success of deep learning is attributed to large-scale clean high-quality labeled data, which is generally unavailable in drug discovery practices. In this thesis, we address this issue by proposing an end-to-end deep learning framework in a semi supervised learning fashion. That is said, the proposed deep learning approach can utilize both labeled and unlabeled data. While labeled data is of very limited availability, the amount of available unlabeled data is generally huge. …