Computational Methods for Structure-Activity Relationship Analysis and Activity Prediction

146 Pages·2015·8.93 MB·English

Computational Methods for
Structure-Activity Relationship
Analysis and Activity Prediction
Kumulative Dissertation
zur Erlangung des Doktorgrades (Dr. rer. nat.)
der Mathematisch-Naturwissenschaftlichen Fakulta¨t
der Rheinischen Friedrich-Wilhelms-Universita¨t Bonn
vorgelegt von
Disha Gupta-Ostermann
aus Kota, Indien
Bonn
May, 2015

Angefertigt mit Genehmigung der
Mathematisch-Naturwissenschaftliche Fakulta¨t der Rheinischen
Friedrich-Wilhelms-Universita¨t Bonn
1. Referent: Univ.-Prof. Dr. rer. nat. Ju¨rgen Bajorath
2. Referent: Univ.-Prof. Dr. rer. nat. Michael Gu¨tschow
Tag der Promotion: 20 October, 2015
Erscheinungsjahr: 2015

Abstract
Structure-activityrelationship(SAR)analysisofsmallbioactivecompounds
is a key task in medicinal chemistry. Traditionally, SARs were established on
a case-by-case basis. However, with the arrival of high-throughput screening
(HTS) and synthesis techniques, a surgein the size and structuralheterogeneity
of compound data is seen and the use of computational methods to analyse
SARs has become imperative and valuable.
In recent years, graphical methods have gained prominence for analysing
SARs. The choice of molecular representation and the method of assessing
similarities affects the outcome of the SAR analysis. Thus, alternative meth-
ods providing distinct points of view of SARs are required. In this thesis, a
novel graphical representation utilizing the canonical scaffold-skeleton defini-
tion to explore meaningful global and local SAR patterns in compound data is
introduced.
Furthermore, efforts have been made to go beyond descriptive SAR analysis
offered by the graphical methods. SAR features inferred from descriptive meth-
odsareutilizedforcompoundactivitypredictions. Inthiscontext, adatastruc-
ture called SAR matrix (SARM), which is reminiscent of conventional R-group
tables, is utilized. SARMs suggest many virtual compounds that represent as
of yet unexplored chemical space. These virtual compounds are candidates for
further exploration but are too many to prioritize simply on the basis of visual
inspection. Conceptually different approaches to enable systematic compound
prediction and prioritization are introduced. Much emphasis is put on evolving
the predictive ability for prospective compound design. Going beyond SAR
analysis, the SARM method has also been adapted to navigate multi-target
spaces primarily for analysing compound promiscuity patterns. Thus, the orig-
inal SARM methodology has been further developed for a variety of medicinal
chemistry and chemogenomics applications.

Acknowledgments
I would like to express deep gratitude to my supervisor Prof. Dr. Ju¨rgen
Bajorathforprovidingmewiththisexcellentopportunitytopursuethedoctoral
studies and for his constant guidance and support.
I thank Prof. Dr. Michael Gu¨tschow for reviewing my thesis as a co-referent. I
also thank Prof. Dr. Thorsten Lang and Prof. Dr. Thomas Schultz for being
members of the review committee.
I extend my gratitude to all the colleagues of the LSI group for providing a nice
working and learning atmosphere. I further thank Jenny Balfer, Dr. Ye Hu and
Dr. Vigneshwaran Namasivayam for the fruitful collaborations. Special thanks
to the lunch group for all the fun times spent in the Mensa.
IwouldliketothankBoehringerIngelheimforsupportingthisthesis. Especially
I’d like to thank Dr. Peter Haebel and Dr. Nils Weskamp for the helpful
discussions and their hospitality.
Further, Iwouldliketothankmyfamilyforshoweringtheirloveonme. Finally,
I would like to thank Bj¨orn and his family, for being a persistent support during
my studies.

Contents
1 Introduction 1
Molecular Representations and Similarity . . . . . . . . . . . . . . . . 1
SAR Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Activity Landscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Multi-Target Activity Spaces . . . . . . . . . . . . . . . . . . . . . . 18
Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
References 23
2 Introducing the LASSO Graph for Compound Data Set Rep-
resentation and Structure-Activity Relationship Analysis 31
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3 Second Generation SAR Matrices 43
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4 Systematic Mining of Analog Series with Related Core Struc-
tures in Multi-Target Activity Space 61
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
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CONTENTS
5 Neighborhood-Based Prediction of Novel Active Compounds
from SAR Matrices 75
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6 Hit Expansion from Screening Data Based upon Conditional
Probabilities of Activity Derived from SAR Matrices 89
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7 Prospective Compound Design using the SAR Matrix-Derived
Conditional Probabilities of Activity 107
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
8 Conclusions 125
Additional References 129
Additional Publications 131
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