Date of Award
6-1-2023
Publication Type
Thesis
Degree Name
M.Sc.
Department
Computer Science
Keywords
Drug Target Binding Affinity;Generative Adversarial Network;SELFIES;Variational Autoencoder
Supervisor
Alioune Ngom
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
A crucial step in drug discovery is identifying drug-target interactions. Over the years, there have been many computational methods to determine whether a drug and a target will interact or not. Drug-target binding affinity can also be determined by predicting the strength of the binding interaction between the drug and the target. Drug target binding affinity consider a lot of information that is left out by drug target interaction. There have been many methods to predict the binding affinity, all the methods use SMILES representation, learning accurate drug representations is essential for tasks such as computational drug repositioning, drug target affinity, drug target interaction, and drug repurposing. There are multiple ways to represent a drug in computational methods, one of which is string-based, and one of the most widely used methods is SMILES. However, SMILES has a few limitations due to its complex grammar. Here in this paper, we change the representation to SELFIES (SELF-referencIng Embedded Strings) to determine if changing drug representation helps in improving the model. We developed a model (DTA+VAE) to predict binding affinity by using a variational auto-encoder and a pre-trained protein model.
Recommended Citation
Patel, Yakin, "DTA+VAE: Drug Target Affinity prediction with SELFIES String via variational autoencoder and Transformer6 protein model" (2023). Electronic Theses and Dissertations. 9373.
https://scholar.uwindsor.ca/etd/9373