Drug discovery for misfolding diseases using structure-based iterative learning
type: CreativeWork
description: Computational methods such as machine learning hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. The high attrition rate of neurodegenerative drug discovery is especially acute for Parkinson’s disease, where no disease-modifying drugs have yet been approved. Numerous clinical trials targeting α-synuclein aggregation, a process implicated in Parkinson’s disease and other synucleinopathies, have failed, at least in part due to the challenges in identifying potent compounds in preclinical investigations. In Chapter 2, I describe machine learning approaches to identify small molecule inhibitors of α-synuclein aggregation to address this problem. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation from fibril surfaces, we aim to identify compounds that bind the catalytic sites on the surface of the mature fibrillar aggregates (the end point polymers of the aggregation process). This prevents the formation of the toxic intermediate aggregate species, termed misfolded oligomers. Fibrils assume different structural polymorphs depending on the synucleinopathy, likely due to the different locations of the nervous system that these diseases occur within. Each tissue has an associated set of specific conditions which likely shape the final structure of the aggregates. Targeting these pathogenic polymorphs may help ameliorate disease progression more effectively than prior efforts. To achieve this goal, I use structure-based machine learning in an iterative manner to first identify and then progressively optimise secondary nucleation inhibitors. Training data for aggregation inhibition were obtained by an assay specifically isolating secondary nucleation, the major mechanism of toxic oligomer production. My results demonstrate that this a…
abstract: Computational methods such as machine learning hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. The high attrition rate of neurodegenerative drug discovery is especially acute for Parkinson’s disease, where no disease-modifying drugs have yet been approved. Numerous clinical trials targeting α-synuclein aggregation, a process implicated in Parkinson’s disease and other synucleinopathies, have failed, at least in part due to the challenges in identifying potent compounds in preclinical investigations. In Chapter 2, I describe machine learning approaches to identify small molecule inhibitors of α-synuclein aggregation to address this problem. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation from fibril surfaces, we aim to identify compounds that bind the catalytic sites on the surface of the mature fibrillar aggregates (the end point polymers of the aggregation process). This prevents the formation of the toxic intermediate aggregate species, termed misfolded oligomers. Fibrils assume different structural polymorphs depending on the synucleinopathy, likely due to the different locations of the nervous system that these diseases occur within. Each tissue has an associated set of specific conditions which likely shape the final structure of the aggregates. Targeting these pathogenic polymorphs may help ameliorate disease progression more effectively than prior efforts. To achieve this goal, I use structure-based machine learning in an iterative manner to first identify and then progressively optimise secondary nucleation inhibitors. Training data for aggregation inhibition were obtained by an assay specifically isolating secondary nucleation, the major mechanism of toxic oligomer production. My results demonstrate that this a…
author: Horne, Robert
language: eng
keywords: Aggregation, Aggregation inhibition, Alzheimer's Disease, Amyloid, Diagnostics, Machine learning, Nanopore, Neurodegeneration, Parkinson's Disease, PET tracers, Protein oligomers, Single molecule, Small molecules, Therapeutics
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/7c24ac62-cc6f-46b0-830f-0c70a635c3e1/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2023-10-06
date modified: 2026-07-22T22:24:13Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.106944