Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 9 of 9 for “"Molecular Property Prediction"”.
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Message passing neural networks for molecular property prediction
… relies heavily on understanding the various molecular properties of potential drug candidates. While experimental assays performed in the lab are the best source of information about molecular properties, these assays are slow and expensive. For this reason, there has been great interest in …
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Data Analytics and Machine Learning Applications in Fermentation Processes and Molecular Property Prediction
… been playing a crucial role in bioprocesses and molecular property prediction. Our study encompasses three main aspects: 1) using data analytics to analyze the occurrence of foaming in batch fermentation processes using multiway partial least square (MPLS) approaches; 2) using hyperparameter …
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A Variational Lower Bound to Mitigate Batch Effect in Molecular Representations
… deal with batch effects and obtain refined molecular representations. InfoCORE establishes a variational lower bound on the conditional mutual information of the latent representations given a batch identifier. Experiments on drug screening data reveal InfoCORE’s superior performance in a …
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Improving ligand discovery using deep learning on three-dimensional structural data
… in various stages of drug discovery, such as molecular property prediction or goal-directed molecular generation. Incorporating three-dimensional (3D) structural data allows to steer prediction and generation towards 3D-dependant properties that are fundamental in hit identification and lead …
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Investigations into Message Passing Neural Networks and Polymer Fouling
… the diverse investigations of high-fidelity molecular property prediction with Message Passing Neural Networks (MPNNs) and nanoscale polymer fouling experimentation with Quartz Crystal Microbalances (QCMs). Message Passing Neural Networks are promising deep learning architectures for chemical …
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Bridging Deep Learning and Probabilistic Inference: Towards Data Efficiency, Identifiability, and Sampling Scalability
… of deep neural networks for few-shot molecular property prediction and optimisation tasks. Next, we analyse the theoretical properties of neural network representations learned across multiple tasks within a probabilistic framework, establishing conditions under which neural networks …
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Machine Learning Methods for Discovering Metabolite Structures from Mass Spectra
… for supervised deep learning including (A) molecular formula prediction, (B) spectrum-to-molecule property prediction, (C) molecule-to-spectrum prediction, and (D) de novo generation of molecular candidates. To address these various tasks, I first introduce the Molecular Formula Transformer …
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Topological Deep Learning: Graphs, Complexes, Sheaves
… We show their additional flexibility benefits molecular applications, where the resulting models outperform prior art on molecular property prediction tasks. The third work proposes a general topological framework for constructing graph coarsening (aka pooling) operators in a way that naturally …
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Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes
… for fitting such datasets. GPs can make predictions with consideration of uncertainty, for example in the virtual screening of molecules and materials, and can also make inferences about incomplete data such as the latent emission signature from a black hole accretion disc. Furthermore, …