Extension of Similarity Functions and their Application toChemical Informatics Problems

Bibliographic Details
Main Author:	Wood, Nicholas Linder
Language:	English
Published:	The Ohio State University / OhioLINK 2018
Subjects:	Information Science Mathematics Molecular Chemistry Pharmacy Sciences Statistics Theoretical Mathematics Chemical Informatics Chemoinformatics Cheminformatics QSAR Domain of Applicability Machine Learning Kernel Linear Algebra Positive Definite
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=osu1542299336598615

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-osu15422993365986152021-08-03T07:08:53Z Extension of Similarity Functions and their Application toChemical Informatics Problems Wood, Nicholas Linder Information Science Mathematics Molecular Chemistry Pharmacy Sciences Statistics Theoretical Mathematics Chemical Informatics Chemoinformatics Cheminformatics QSAR Domain of Applicability Machine Learning Kernel Linear Algebra Mathematics Positive Definite Similarity is the most pervasive concept in chemoinformatics and it providesdirection for many of the problems which arise in that field. Similarity functionsare mathematical tools for quantifying the similarity of one molecule with respectto another molecule. In this work, we developed a method for the quantificationof the similarity of one molecule with respect to a set of molecules. This methodrequires a similarity function which is symmetric and positive definite. If thesimilarity function meets two additional mild requirements, namely if it is boundbetween zero and unity and is unity when evaluated on two identical molecules,then we say that the similarity function is extendable. In this case, the similarityof a molecule with respect to a set containing one molecule reduces to theoriginal similarity function evaluated on those two molecules. We additionallystated and proved several properties of the extension of similarity functions.We then applied the extension of similarity functions to two problems inchemoinformatics. First, we used the extension of similarity functions as thebasis for machine learning models for the prediction of various molecularendpoints. These machine learning models were compared to the kNN machinelearning model. For each endpoint predicted, the model based on the extensionof similarity functions was shown either comparable to or to be exceeding thekNN model. Second, we used the extension of similarity functions as the basisfor defining the domain of applicability of a machine learning model. We appliedthis definition to a kNN model and showed that using the extension of similarityfunctions can be used to order predictions for the rational selection of moleculesfor further testing. We showed how doing so can increase the overall usefulnessof a machine learning model.Finally, we stated several mathematical questions related to the extension ofsimilarity functions which, if answered, could aid in the training of machinelearning models based on the extension of similarity functions. 2018 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1542299336598615 http://rave.ohiolink.edu/etdc/view?acc_num=osu1542299336598615 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Information Science Mathematics Molecular Chemistry Pharmacy Sciences Statistics Theoretical Mathematics Chemical Informatics Chemoinformatics Cheminformatics QSAR Domain of Applicability Machine Learning Kernel Linear Algebra Mathematics Positive Definite
spellingShingle	Information Science Mathematics Molecular Chemistry Pharmacy Sciences Statistics Theoretical Mathematics Chemical Informatics Chemoinformatics Cheminformatics QSAR Domain of Applicability Machine Learning Kernel Linear Algebra Mathematics Positive Definite Wood, Nicholas Linder Extension of Similarity Functions and their Application toChemical Informatics Problems
author	Wood, Nicholas Linder
author_facet	Wood, Nicholas Linder
author_sort	Wood, Nicholas Linder
title	Extension of Similarity Functions and their Application toChemical Informatics Problems
title_short	Extension of Similarity Functions and their Application toChemical Informatics Problems
title_full	Extension of Similarity Functions and their Application toChemical Informatics Problems
title_fullStr	Extension of Similarity Functions and their Application toChemical Informatics Problems
title_full_unstemmed	Extension of Similarity Functions and their Application toChemical Informatics Problems
title_sort	extension of similarity functions and their application tochemical informatics problems
publisher	The Ohio State University / OhioLINK
publishDate	2018
url	http://rave.ohiolink.edu/etdc/view?acc_num=osu1542299336598615
work_keys_str_mv	AT woodnicholaslinder extensionofsimilarityfunctionsandtheirapplicationtochemicalinformaticsproblems
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Extension of Similarity Functions and their Application toChemical Informatics Problems

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