Quantifying the diversity of bioactive small molecule sets
type: CreativeWork
description: The concept of molecular diversity is used in a range of different contexts in cheminformatics, including choosing compound sets for screening, measuring the success of generative AI models, and analysing and comparing new or existing compound sets. Despite its frequent use in multiple different contexts, no gold standard method to quantify diversity exists. A multitude of molecular representations and strategies to quantify diversity are used, and there is limited knowledge of how the numerous diversity measures compare. This thesis focuses on the use of diversity measures to quantify and compare the diversity of sets of bioactive small molecules. A representative set of diversity measures based on scaffolds and fingerprints was chosen and implemented together with a diversity measure based on reduced graphs. To analyse the diversity of bioactive small molecules, a carefully designed dataset of interacting compound-target pairs was extracted from the open-source bioactivity database ChEMBL. Interacting compound-target pairs were identified by combining information from measured activities and a manually curated set of mechanisms of action in ChEMBL. The extraction workflow is fully automated and reproducible and can be performed for all ChEMBL versions from ChEMBL 26 onwards. Before analysing the diversity of compounds in the compound-target dataset, it was investigated how well the chosen diversity measures are able to quantify the diversity of bioactive small molecules. Diversity measures are frequently used to compare compound sets of various sizes. Using ChEMBL data, it was analysed whether dataset size had a strong influence on any of the measures. Furthermore, most diversity measures quantify the structural diversity of the compounds in a compound set, but they are commonly used to predict the functional diversity of a compound set, i.e., how many different targets the compounds can potentially interact with. Using the compound-target dataset, it was examined…
abstract: The concept of molecular diversity is used in a range of different contexts in cheminformatics, including choosing compound sets for screening, measuring the success of generative AI models, and analysing and comparing new or existing compound sets. Despite its frequent use in multiple different contexts, no gold standard method to quantify diversity exists. A multitude of molecular representations and strategies to quantify diversity are used, and there is limited knowledge of how the numerous diversity measures compare. This thesis focuses on the use of diversity measures to quantify and compare the diversity of sets of bioactive small molecules. A representative set of diversity measures based on scaffolds and fingerprints was chosen and implemented together with a diversity measure based on reduced graphs. To analyse the diversity of bioactive small molecules, a carefully designed dataset of interacting compound-target pairs was extracted from the open-source bioactivity database ChEMBL. Interacting compound-target pairs were identified by combining information from measured activities and a manually curated set of mechanisms of action in ChEMBL. The extraction workflow is fully automated and reproducible and can be performed for all ChEMBL versions from ChEMBL 26 onwards. Before analysing the diversity of compounds in the compound-target dataset, it was investigated how well the chosen diversity measures are able to quantify the diversity of bioactive small molecules. Diversity measures are frequently used to compare compound sets of various sizes. Using ChEMBL data, it was analysed whether dataset size had a strong influence on any of the measures. Furthermore, most diversity measures quantify the structural diversity of the compounds in a compound set, but they are commonly used to predict the functional diversity of a compound set, i.e., how many different targets the compounds can potentially interact with. Using the compound-target dataset, it was examined…
author: Heinzke, Anna Lina
language: eng
keywords: Cheminformatics, Chemoinformatics, Drug discovery
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/a53a9d56-1b47-4a22-be25-3c3b9d9ebc7d/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2024-10-07
date modified: 2026-07-22T22:24:06Z
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.117851