Diamond Energy - A systematic conformation searching method
type: CreativeWork
description: Fully understanding the properties of molecules requires a good knowledge of all low-energy conformations. A python program, Diamond Energy, was developed based on an assumption that the structures of local minima are similar to a diamond lattice. These conformers could be found by calculations with integer arithmetic and evaluated with simple energy equations. Both of these should speed up systematic searching. Test results on alkane showed that Diamond Energy was able to do systematic conformational searching for acyclic alkanes exhaustively, accurately and very fast. The scope of the program’s application was extended beyond acyclic alkanes to include six-membered rings, and then further extended to include saccharides. This extension was facilitated by the development of an automatic pipeline for searching suitable energy evaluation parameters when introducing new atom types. Finally, using the large quantity of data generated by the Diamond Energy test process, a machine learning pipeline was applied and fed with Diamond Energy data. This made it possible to explore the geometry distribution of diamond lattice framework and help to improve the conformational searching process.
abstract: Fully understanding the properties of molecules requires a good knowledge of all low-energy conformations. A python program, Diamond Energy, was developed based on an assumption that the structures of local minima are similar to a diamond lattice. These conformers could be found by calculations with integer arithmetic and evaluated with simple energy equations. Both of these should speed up systematic searching. Test results on alkane showed that Diamond Energy was able to do systematic conformational searching for acyclic alkanes exhaustively, accurately and very fast. The scope of the program’s application was extended beyond acyclic alkanes to include six-membered rings, and then further extended to include saccharides. This extension was facilitated by the development of an automatic pipeline for searching suitable energy evaluation parameters when introducing new atom types. Finally, using the large quantity of data generated by the Diamond Energy test process, a machine learning pipeline was applied and fed with Diamond Energy data. This made it possible to explore the geometry distribution of diamond lattice framework and help to improve the conformational searching process.
author: Wei, Mengman
language: eng
keywords: Cheminformatics, Conformation Searching, Diamond Lattice, Systematic Search, Tree Searching
license: https://www.repository.cam.ac.uk/bitstreams/353f4d43-6c08-4177-bc26-b06d5318d259/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2024-01-05
date modified: 2026-07-22T22:23:57Z
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.109200