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Full-Text Articles in Life Sciences
Optimization And Application Of Graph Neural Networks, Shuo Zhang
Optimization And Application Of Graph Neural Networks, Shuo Zhang
Dissertations, Theses, and Capstone Projects
Graph Neural Networks (GNNs) are widely recognized for their potential in learning from graph-structured data and solving complex problems. However, optimal performance and applicability of GNNs have been an open-ended challenge. This dissertation presents a series of substantial advances addressing this problem. First, we investigate attention-based GNNs, revealing a critical shortcoming: their ignorance of cardinality information that impacts their discriminative power. To rectify this, we propose Cardinality Preserved Attention (CPA) models that can be applied to any attention-based GNNs, which exhibit a marked improvement in performance. Next, we introduce the Directional Node Pair (DNP) descriptor and the Robust Molecular Graph …
Machine Learning And Solvation Theory For Drug Discovery, Lieyang Chen
Machine Learning And Solvation Theory For Drug Discovery, Lieyang Chen
Dissertations, Theses, and Capstone Projects
Drug discovery is a notoriously expensive and time-consuming process; hence, developing computational methods to facilitate the discovery process and lower the associated costs is a long-sought goal of computational chemists. Protein-ligand binding, which provides the physical and chemical basis for the mechanism of action of most drugs, occurs in an aqueous environment, and binding affinity is determined not only by atomic interactions between the protein and ligand but also by changes in their interactions with surrounding water molecules that occur upon binding. Thus, a quantitative understanding of the roles water molecules play in the protein-ligand binding process is an essential …
Machine Learning Applications For Drug Repurposing, Hansaim Lim
Machine Learning Applications For Drug Repurposing, Hansaim Lim
Dissertations, Theses, and Capstone Projects
The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected …