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 6 of 6 for “"drug-target interaction"”.
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Predicting drug - target interaction network using deep learning models for drug repurposing through genetic information
… that biological and experimental methods for drug discovery are time-consuming and expensive. New efforts have been explored to perform drug repurposing through predicting drug-target interaction networks using biological and chemical properties of drugs and targets. However, due to the …
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Tackling the bigger picture in computational drug design : theory, methods, and application to HIV-1 protease and erythropoietin systems
This thesis addresses challenging aspects of drug design that require explicit consideration of more than a single drug-target interaction in an unchanging environment. In the first half, the common challenge of designing a molecule that recognizes a desired subset of target molecules amidst a …
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Identifying drug-target and drug-disease associations using computational intelligence
Background: Traditional drug development is an expensive process that typically requires the investment of a large number of resources in terms of finances, equipment, and time. However, sometimes these efforts do not result in a pharmaceutical product in the market. To overcome the limitations of …
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Graph Representation Learning for Drug Discovery
Drug discovery is an expensive and labor-intensive process, typically taking an average of 10–15 years. The goal of this thesis is to substantially accelerate this process by developing machine learning (ML) algorithms for three key steps in drug discovery pipeline. First, we develop better …
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DLRNA-BERTa: A transformer approach for RNA-drug interaction prediction
… more and more attention due to their ability to target a variety of diseases, including many rare conditions. In this evolving landscape, Bidirectional Encoder Representations from Transformers (BERT) models offer a promising, cost-effective, and efficient approach to accelerate RNA-targeted drug …
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Deep learning of proteomics data
… a state-of-the-art approach to modelling the interactions between proteins and drugs. In this chapter, we leverage a set of BERT-style models that have been pre-trained on vast quantities of both protein and drug data. The encodings produced by each model are then utilised as node …