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 8 of 8 for “"molecular generation"”.
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Equivariant Autoregressive Models for Molecular Generation
In-silico generation of diverse molecular structures has emerged as a promising method to navigate the complex chemical landscape, with direct applications to inverse material design and drug discovery. However, 3D molecular structure generation comes with several unique challenges; generated …
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Conservation of Timeless in the Mammalian Circadian Clock
… on the negative feedback arm of the mammalian molecular clockwork. This models supports conservation of transcriptional elements that are the basis for the molecular generation of circadian rhythms.
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Software Library for Generative Model Applications
The generation of data by machine learning models is a powerful concept that has impacted the field of Artificial Intelligence in the past few years. In this thesis, we focus on building a software library to facilitate the workflow, evaluation, and analysis of generative models. Our work is …
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Combining the Power of Attention Models and Many-objective Computational Intelligence Algorithms for Drug Design
… contrastive Transformer-based latent models for molecular generation. Third, it applies many-objective computational intelligence algorithms in the continuous latent space generated by a Transformer model to generate optimal drug candidates that fulfill ADMET and other essential properties in …
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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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Neural graph representation learning with application to chemistry
… for learning continuous representation of molecular graphs, a much more compact representation than traditional fingerprints. We demonstrate its better predictive performance in two tasks. First, we seek to automate the prediction of organic reaction outcomes. The previous solution utilizes …
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Molecular Graph Representation Learning and Generation for Drug Discovery
… and to represent the complexities of the molecular landscape, these hand-engineered rules prove insufficient. Deep learning models are powerful because they learn the important statistical features of the problem–but only with the correct inductive biases. We tackle this important problem …
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Guiding Deep Probabilistic Models
… demonstrate improved training stability in image generation tasks. In the second part of the thesis, we introduce distribution support alignment as an alternative to the distribution alignment objective and develop a learning algorithm that guides distributions towards support alignment. We …