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Full-Text Articles in Biomedical Engineering and Bioengineering
Augmenting Structure/Function Relationship Analysis With Deep Learning For The Classification Of Psychoactive Drug Activity At Class A G Protein-Coupled Receptors, Hannah Willow Shows
Augmenting Structure/Function Relationship Analysis With Deep Learning For The Classification Of Psychoactive Drug Activity At Class A G Protein-Coupled Receptors, Hannah Willow Shows
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G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used …
Multi-Label Classification On Locally-Linear Data: Application To Chemical Toxicity Prediction, Xiu Huan Yap
Multi-Label Classification On Locally-Linear Data: Application To Chemical Toxicity Prediction, Xiu Huan Yap
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Computational models may assist in identification and prioritization of large chemical libraries. Recent experimental and data curation efforts, such as from the Tox21 consortium, have contributed towards toxicological datasets of increasing numbers of chemicals and toxicity endpoints, creating a golden opportunity for the exploration of multi-label learning and deep learning approaches in this thesis. Multi-label classification (MLC) methods may improve model predictivity by accounting for label dependence. However, current measures of label dependence, such as correlation coefficient, are inappropriate for datasets with extreme class imbalance, often seen in toxicological datasets. In this thesis, we propose a novel label dependence measure …
Augmenting Structure/Function Relationship Analysis With Deep Learning For The Classification Of Psychoactive Drug Activity At Class A G Protein-Coupled Receptors, Hannah Willow Shows
Augmenting Structure/Function Relationship Analysis With Deep Learning For The Classification Of Psychoactive Drug Activity At Class A G Protein-Coupled Receptors, Hannah Willow Shows
Browse all Theses and Dissertations
G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used …