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Latent Spaces for Antimicrobial Peptide Design


Latent Spaces for Antimicrobial Peptide Design

Renaud, Samuel (2023) Latent Spaces for Antimicrobial Peptide Design. Masters thesis, Concordia University.

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Current antibacterial treatments cannot overcome the growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have emerged as a powerful method for generating novel therapeutic candidates from existing datasets; however, there has been less research focused on evaluating the search spaces associated with these generators. In this research I employ five deep learning model architectures for de novo generation of antimicrobial peptide sequences and assess the properties of their associated latent spaces. I train a RNN, RNN with attention, WAE, AAE and Transformer model and compare their abilities to construct desirable latent spaces in 32, 64, and 128 dimensions. I assess reconstruction accuracy, generative capability, and model interpretability and demonstrate that while most models are able to create a partitioning in their latent spaces into regions of low and high AMP sampling probability, they do so in different manners and by appealing to different underlying physicochemical properties. In this way I demonstrate several benchmarks that must be considered for such models and suggest that for optimization of search space properties, an ensemble methodology is most appropriate for design of new AMPs. I design an AMP discovery pipeline and present candidate sequences and properties from three models that achieved high benchmark scores. Overall, by tuning models and their accompanying latent spaces, targeted sampling of anti-microbial peptides with ideal characteristics is achievable.

Divisions:Concordia University > Faculty of Arts and Science > Physics
Item Type:Thesis (Masters)
Authors:Renaud, Samuel
Institution:Concordia University
Degree Name:M. Sc.
Date:2 March 2023
Thesis Supervisor(s):Mansbach, Rachael
Keywords:Drug Design, Artificial Intelligence, Antimicrobial Peptides, AMP, Variational Autoencoder, Generative Model, Transformer, Latent Space, Principal Components Analysis, PCA.
ID Code:991951
Deposited By: Samuel Renaud
Deposited On:21 Jun 2023 14:53
Last Modified:21 Jun 2023 14:53


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