Improving de novo molecule generation for structure-based drug design
type: CreativeWork
description: *De novo* molecule generation for drug design has seen a resurgence in recent years, mostly due to the rapid advances in machine learning (ML) algorithms that utilise deep neural networks, resulting in a plethora of ML-based generative models. However, there is often a large disparity in published evaluations and applications of such approaches compared to the practical needs of real drug design projects (for example, optimizing QED *versus* optimizing binding affinity commonly approximated by structure-based approaches). Moreover, the density of approaches and often lack of relevant, standardized objectives makes it difficult to truly discern “state-of-the-art”. The work in this thesis aims to address some of these issues and improve the applicability and evaluation of *de novo* molecule generation for practical drug design. The first research chapter will outline the design and use of an open-source python-based software named MolScore. This configurable suite of scoring functions (including an interface to 5 docking algorithms and ~2,300 trained bioactivity models) can be used to design difficult yet relevant drug design objectives for standardized comparison, or practical usage with generative models. In addition, MolScore includes a graphical user interface to improve usability and a suite of common evaluation metrics to evaluate *de novo* generated molecules. Next, MolScore was implemented to compare the use of docking as a more difficult objective function for REINVENT (a generative model for goal-directed *de novo* molecule generation), as opposed to more commonly used predictive models of molecule bioactivity. This resulted in increased diversity of *de novo* molecules and improved coverage of known bioactive chemical space. However, the added computational expense required for generative model optimization is a practical disadvantage of docking as a scoring function. To address the computational expense of optimizing docking scores, a hybrid reinforcement …
abstract: *De novo* molecule generation for drug design has seen a resurgence in recent years, mostly due to the rapid advances in machine learning (ML) algorithms that utilise deep neural networks, resulting in a plethora of ML-based generative models. However, there is often a large disparity in published evaluations and applications of such approaches compared to the practical needs of real drug design projects (for example, optimizing QED *versus* optimizing binding affinity commonly approximated by structure-based approaches). Moreover, the density of approaches and often lack of relevant, standardized objectives makes it difficult to truly discern “state-of-the-art”. The work in this thesis aims to address some of these issues and improve the applicability and evaluation of *de novo* molecule generation for practical drug design. The first research chapter will outline the design and use of an open-source python-based software named MolScore. This configurable suite of scoring functions (including an interface to 5 docking algorithms and ~2,300 trained bioactivity models) can be used to design difficult yet relevant drug design objectives for standardized comparison, or practical usage with generative models. In addition, MolScore includes a graphical user interface to improve usability and a suite of common evaluation metrics to evaluate *de novo* generated molecules. Next, MolScore was implemented to compare the use of docking as a more difficult objective function for REINVENT (a generative model for goal-directed *de novo* molecule generation), as opposed to more commonly used predictive models of molecule bioactivity. This resulted in increased diversity of *de novo* molecules and improved coverage of known bioactive chemical space. However, the added computational expense required for generative model optimization is a practical disadvantage of docking as a scoring function. To address the computational expense of optimizing docking scores, a hybrid reinforcement …
author: Thomas, Morgan
language: eng
keywords: De novo molecule generation, Drug design, Generative models, Reinforcement learning, Structure-based drug design
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/091b44da-827d-4ccf-9d35-540f1d5fd85e/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2023-08-03
date modified: 2026-07-22T22:24:08Z
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.107998