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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5587_Библиотеки_им_академика_М_И_Перельмана.pdf
Extended Topochemical Atom (ETA) indices, developed by the authors’ group, successfully address
the aspects of molecular topology, electronic information, and different types of bonded interactions,
and have been extensively employed for the modeling of different types of activity/property and toxicity
endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability
of the ETA indices for a predictive modeling paradigm.
Chapter 3
Evolution of Multivariate Image Analysis in QSAR: The Case for a Neglected Disease ....................84
Matheus P. Freitas, Federal University of Lavras, Brazil
Mariene H. Duarte, Federal University of Lavras, Brazil
Multivariate Image Analysis applied in Quantitative Structure-Activity Relationship (MIA-QSAR) is
a simple method to achieve, at least in a variety of examples, QSAR models with predictive abilities
comparable to those of sophisticated tridimensional methodologies. MIA-QSAR is based on the
correlation between properties (e.g. biological activities) and chemical descriptors, which are pixels of
images representing chemical structures in a congeneric series of molecules. The MIA-QSAR approach
has been improved since its creation, in 2005, both in terms of data analysis and development of more
descriptive information. This chapter reports the MIA-QSAR method, including its augmented version,
named aug-MIA-QSAR because of the introduction of new dimensions to better encode atomic properties.
In addition, the application to a case study illustrates the main practical differences between traditional
and augmented MIA-QSAR. The use of a neglected disease as example represents a challenge in QSAR,
which is particularly focused on diseases with higher economical appearance.
Chapter 4
Quantitative Structure-Activity/Property/Toxicity Relationships through Conceptual Density
Functional Theory-Based Reactivity Descriptors ...............................................................................123
Sudip Pan, Indian Institute of Technology Kharagpur, India
Ashutosh Gupta, Udai Pratap Autonomous College, India
Venkatesan Subramanian, Central Leather Research Institute, India
Pratim K. Chattaraj, Indian Institute of Technology Kharagpur, India
Developing effective structure-activity/property/toxicity relationships (QSAR/QSPR/QSTR) is very helpful
in predicting biological activity, property, and toxicity of a given set of molecules. Regular change in
these properties with the structural alteration is the main reason to obtain QSAR/QSPR/QSTR models.
The advancement in making different QSAR/QSPR/QSTR models to describe activity, property, and
toxicity of various groups of molecules is reviewed in this chapter. The successful implementation of
Conceptual Density Functional Theory (CDFT)-based global as well as local reactivity descriptors in
modeling effective QSAR/QSPR/QSTR is highlighted.
Chapter 5
Importance of Applicability Domain of QSAR Models ..................................................................... 180
Kunal Roy, Jadavpur University, India
Supratik Kar, Jadavpur University, India
Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery,
environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals.
One of the principles recommended by the Organization of Economic Co-operation and Development
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(OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which
allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the
training compounds, which are used in the model development. The AD is a significant tool to build a
reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the
training compounds. Thus, characterization of interpolation space is significant in defining the AD. An
attempt is made in this chapter to address the important concepts and methodology of the AD as well as
criteria for estimating AD through training set interpolation in the descriptor space.
Chapter 6
QSAR of Antioxidants ........................................................................................................................ 212
Omar Deeb, Al-Quds University, Palestine
Mohammad Goodarzi, Katholieke Universiteit Leuven, Belgium
Antioxidants are substances that protect cells from the damaging effects of oxygen radicals, which are
chemicals that play a part in some diseases such as cancer and others. Antioxidants are expected to be
promising drugs in the management of these diseases by removing oxidative stress. Most of the modeling
approaches involved in designing new antioxidants is based on Quantitative Structure-Activity Relationship
(QSAR). A number of QSAR studies have been conducted to elucidate the structural requirements of
antioxidants for their activities in order to predict the potency of these compounds with regard to the
targeted activity and to direct the synthesis of more potent analogues. The main focus of this chapter
is on the QSAR modeling of antioxidant compounds. The authors provide different QSAR studies of
antioxidant compounds and try to compare between them in terms of the best models obtained and their
use in designing potential new drugs.
Chapter 7
QSAR Studies on Bacterial Efflux Pump Inhibitors ........................................................................... 238
Khac-Minh Thai, University of Medicine and Pharmacy at HCMC, Vietnam
Trong-Nhat Do, University of Medicine and Pharmacy at HCMC, Vietnam
Thuy-Viet-Phuong Nguyen, University of Medicine and Pharmacy at HCMC, Vietnam
Duc-Khanh-Tho. Nguyen, University of Medicine and Pharmacy at HCMC, Vietnam
Thanh-Dao Tran, University of Medicine and Pharmacy at HCMC, Vietnam
Antimicrobial drug resistance occurs when bacteria undergo certain modifications to eliminate the
effectiveness of drugs, chemicals, or other agents designed to cure infections. To date, the burden of
resistance has remained one of the major clinical concerns as it renders prolonged and complicated
treatments, thereby increasing the medical costs with lengthier hospital stays. Of complex causes for
bacterial resistance, there has been increasing evidence that proved the significant role of efflux pumps
in antibiotic resistance. Coadministration of Efflux Pump Inhibitors (EPIs) with antibiotics has been
considered one of the promising ways not only to improve the efficacy but also to extend the clinical
utility of existing antibiotics. This chapter begins with outlining current knowledge about bacterial efflux
pumps and drug designs applied in identification of their modulating compounds. Following, the chapter
addresses and provides a discussion on Quantitative Structure-Activity Relationship (QSAR) analyses
in search of novel and potent efflux pump inhibitors.
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Chapter 8
Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates: A Case
Study on Fluoroquinolones – Mycobacterium tuberculosis DNA Gyrase Inhibitors ......................... 269
Nikola Minovski, National Institute of Chemistry, Slovenia
Marjana Novič, National Institute of Chemistry, Slovenia
Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term
and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development
of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to
effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of
the current trends and advances in the in silico design of novel drug candidates with a special emphasis
on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase
inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug
discovery approaches including multidimensional machine-learning methods, ligand-based and structurebased methodologies, as well as their proficient combination and integration into an intelligent virtual
screening protocol for design and optimization of novel 6-FQ analogs.
Chapter 9
Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B
Inhibitors ............................................................................................................................................. 318
Khac-Minh Thai, University of Medicine and Pharmacy at HCMC, Vietnam
Quoc-Hiep Dong, University of Medicine and Pharmacy at HCMC, Vietnam
Thi-Thanh-Lan Nguyen, University of Medicine and Pharmacy at HCMC, Vietnam
Duy-Phong Le, University of Medicine and Pharmacy at HCMC, Vietnam
Minh-Tri Le, University of Medicine and Pharmacy at HCMC, Vietnam
Thanh-Dao Tran, University of Medicine and Pharmacy at HCMC, Vietnam
Nonstructural 5B (NS5B) polymerase and Nonstructural 3/4A (NS3/4A) protease have proven to be
promising targets for the development of anti-HCV (Hepatitis C Virus) agents. The NS5B polymerase
is of paramount importance in HCV viral replication; therefore, employing NS5B inhibitors was
considered an effective way for the treatment of HCV. Identifying inhibitors against NS3/4A serine
protease represents another attractive approach applied in anti-HCV drug discovery, which is evidenced
by its crucial role of in the biogenesis of the viral replication activity. In this chapter, many different
computational approaches including Quantitative Structure-Activity Relationship (QSAR) and virtual
screening in anti-HCV drug discovery were considered and discussed in detail. Virtual Screening (VS)
techniques, including ligand-based and structure-based, and QSAR have been utilized for the discovery
of NS5B inhibitors. Moreover, using various in silico protocols and workflows, a number of studies have
been conducted with an aim of identifying potential NS3/4A blockage agents.
Chapter 10
QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease ........... 354
C. Gopi Mohan, Amrita Institute of Medical Sciences and Research Centre, India
Shikhar Gupta, National Institute of Pharmaceutical Education and Research, India
Alzheimer’s Disease (AD) is a multifactorial neurological syndrome with the combination of aging,
genetic, and environmental factors triggering the pathological decline. Interestingly, the importance
of the Acetylcholinesterase (AChE) enzyme has increased due to its involvement in the β-amyloid
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peptide fibril formation during AD pathogenesis. In silico technique, QSAR has proven its usefulness
in pharmaceutical research for the design/optimization of new chemical entities. Further, QSAR method
advanced the scope of rational drug design and the search for the mechanism of drug action. It is a wellestablished fact that the chemical and pharmaceutical effects of a compound are closely related to its
physico-chemical properties, which can be calculated by various methods from the compound structure.
This chapter focuses on different Quantitative Structure-Activity Relationship (QSAR) studies carried
out for a variety of cholinesterase inhibitors for the treatment of AD. These predictive models will be
potentially used for further designing better and safer drugs against AD.
Chapter 11
Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in
Breast Cancer ...................................................................................................................................... 400
Tarun Jha, Jadavpur University, India
Nilanajn Adhikari, Jadavpur University, India
Amit Kumar Halder, Jadavpur University, India
Achintya Saha, University of Calcutta, India
Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen
production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based
Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific
Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here
to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five
or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase
for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors
may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and π-π interactions are
other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR,
CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and
dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.
Chapter 12
Computational Techniques Application in Environmental Exposure Assessment .............................471
Karolina Jagiello, University of Gdansk, Poland
Tomasz Puzyn, University of Gdansk, Poland
In this chapter, the application of computational techniques in environmental exposure assessment
was described. The most important groups of these techniques are Multimedia Mass-balance (MM)
modelling and Quantitative Structure-Activity/Structure-Property Relationships (QSAR/QSPR) modelling.
Multimedia Mass-balance models have been widely utilized for studying Long-Range Transport Potential
(LRTP) and overall persistence (POV) of Persistent Organic Pollutants (POPs), regulated by many
national and international acts, including the Stockholm Convention on POPs. Recently, a novel modelling
methodology that links QSPR and MM has been implemented. According to this approach, the physical/
chemical properties required as the input variables for multimedia modelling can be calculated directly
from appropriate QSPR models. QSPR models must be previously developed based on the relationships
between the chemical structure and the modelled properties (QSPR).
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Chapter 13
QSAR-Based Studies of Nanomaterials in the Environment .............................................................. 506
Valeria V. Kleandrova, University of Porto, Portugal
Feng Luan, Yantai University, China & University of Porto, Portugal
Alejandro Speck-Planche, University of Porto, Portugal
M. Natália D. S. Cordeiro, University of Porto, Portugal
Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the
environment. In recent years, the development of nanotechnology has led to the design and large-scale
production of many new materials and devices with a vast range of applications. However, along with
the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible
health-risks and environmental impacts. This chapter provides an overview of the Quantitative StructureActivity Relationships (QSAR) studies performed so far towards predicting nanoparticles’ environmental
toxicity. Recent progresses on the application of these modeling studies are additionally pointed out.
Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of
ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new
and exciting directions for QSAR modeling are discussed.
Chapter 14
Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of
NanoMaterials ..................................................................................................................................... 535
Eleni Vrontaki, NovaMechanics Ltd., Cyprus & University of Athens, Greece
Thomas Mavromoustakos, University of Athens, Greece
Georgia Melagraki, NovaMechanics Ltd., Cyprus
Antreas Afantitis, NovaMechanics Ltd., Cyprus
In the last few decades, nanotechnology has been deeply established into human’s everyday life with
a great number of applications in cosmetics, textiles, electronics, optics, medicine, and many more.
Although nanotechnology applications are rapidly increasing, the toxicity of some nanomaterials to
living organisms and the environment still remains unknown and needs to be explored. The traditional
toxicological evaluation of nanoparticles with the wide range of types, shapes, and sizes often involves
expensive and time-consuming procedures. An efficient and cheap alternative is the development and
application of predictive computational models using Quantitative Nanostructure-Activity Relationship
(QNAR) methods. Towards this goal, researchers are mainly focused on the adverse effects of metal
oxides and carbon nanotubes, but to date, QNAR studies are rare mainly because of the limited number
of available organized datasets. In this chapter, recent studies for predictive QNAR models for the risk
assessment of nanomaterials are reported and the perspectives of computational nanotoxicology that
deeply relies on the intense collaboration between experimental and computational scientists are discussed.
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Chapter 15
QSPR/QSAR Analyses by Means of the CORAL Software: Results, Challenges, Perspectives ....... 560
Andrey A. Toropov, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Italy
Alla P. Toropova, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Italy
Emilio Benfenati, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Italy
Orazio Nicolotti, Università degli Studi di Bari “Aldo Moro”, Italy
Angelo Carotti, Università degli Studi di Bari “Aldo Moro”, Italy
Karel Nesmerak, Charles University in Prague, Czech Republic
Aleksandar M. Veselinović, University of Niš, Serbia
Jovana B. Veselinović, University of Niš, Serbia
Pablo R. Duchowicz, Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas
INIFTA (UNLP, CCT La Plata-CONICET), Argentina
Daniel Bacelo, Universidad de Belgrano, Argentina
Eduardo A. Castro, Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA
(UNLP, CCT La Plata-CONICET), Argentina
Bakhtiyor F. Rasulev, Jackson State University, USA
Danuta Leszczynska, Jackson State University, USA
Jerzy Leszczynski, Jackson State University, USA
In this chapter, the methodology of building up quantitative structure—property/activity relationships
(QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis
of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation
of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/
QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of
the correlation weights for various features of the molecular structure. Hybrid models that are based on
features extracted from both SMILES and a graph also can be built up by the CORAL software. The
conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR
model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an
endpoint prediction.
Compilation of References ............................................................................................................... 586
About the Contributors .................................................................................................................... 688
Index ................................................................................................................................................... 701
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xvi
Foreword
From the title of this book, we capture the broad extent of this scientific paradigm built around the drug
molecule. This book successfully records the position of drug research, now in its third generation. To
reminisce a moment, we go back to the first generation, the beginning of mathematics and structure
description introduced into the current paradigm. The decades of the 1960s and 1970s witnessed the
origin of mathematics applied to molecular structure with the intent to be predictive in drug design. We
speak of QSAR. The origin of the paradigm in the first generation was built around physical properties
first, followed by numerical descriptors of structure. Included was the use of molecular orbital theory
to encode confirmation, the use of topology to introduce a vast area of structure description, and the
identification of a critical structure feature on an active molecule, called the pharmacophore. These were
the introductory elements in the 1960s and 1970s that underlie where we are today.
A second generation emerged in the 1980s and 1990s, where broad acceptance of these approaches
came into being in academia and industry. Accompanying this acceptance was the development of a
large number of methods seeking information necessary for understanding and drug design. Various
modelling paradigms were introduced leading to insight guiding drug design. With the accompanying
gain in mathematical theories and computer methods, this second generation led to highly successful
strategies that greatly enriched successful drug design.
We are now in the third generation of this grand paradigm. It is characterized by a significant broadening
of the approaches developed earlier. Look at the titles of the articles written by the 50 authors here. They
reflect interest and successful development of research far beyond just drug design. There is predictive
toxicology, risk assessment, nanomaterials, antioxidants, just to name a few. This third generation in
2000-2020 is one of immense dispersion of attention and applications of the paradigm developed earlier.
This book captures the essence of this third generation. It should play an active part in the education
of everyone pursuing the goals that compose this generation of scientists involved in the subjects covered
in this splendid text, expertly assembled by Kunal Roy.
Lemont B. Kier
Virginia Commonwealth University, USA
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Foreword
Lemont B. Kier is an Emeritus Professor of Medicinal Chemistry at the Virginia Commonwealth University, Richmond, Virginia,
USA. Dr. Kier received a BS in Pharmacy from The Ohio State University, USA, and a PhD in Medicinal Chemistry from the
University of Minnesota, USA. Dr. Kier was an early developer of drug design strategies using quantitative structure-activity
information. This included molecular orbital theory to predict preferred conformations and numerical descriptors encoding
molecular structure and electronic properties. More recent research has developed new approaches to the modeling of molecular structure using cellular automata. These include studies of dynamic processes leading to theories of anesthetic action,
sleep, aging, and nerve conduction. Close to 300 papers and 8 books have revealed these contributions. Dr. Kier is active in
medicinal chemistry, life sciences, and nurse anesthesia courses at the Virginia Commonwealth University.
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xvii
xviii
Preface
Quantitative Structure-Activity Relationships (QSARs) represent predictive models derived from application of statistical tools correlating biological activity (including therapeutic and toxic) or other
properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of
molecular structure and/or property. QSAR is a sub-discipline of Cheminformatics, which has long been
used in medicinal chemistry for lead optimization and drug design. It is basically a ligand-based statistical approach. However, with advancement in the knowledge of the target receptor structure information
and related studies, the QSAR studies of the present days are usually performed in combination with
the structure-based approaches leading to a good number of success stories. The knowledge of QSAR
in association with other ligand-based approaches like pharmacophore mapping and structure-based
approaches like docking coupled with virtual screening of compound libraries may identify novel hit
compounds. QSAR increases the probability of success of finding an optimum lead with desired therapeutic efficacy and pharmacokinetic profile, increased selectivity, and minimum side effects at the same
time, thus avoiding costly experiments with thousands of compounds with less potential to become successful, hence avoiding colossal expenditure. QSAR is very helpful in modeling absorption, distribution,
metabolism, elimination, and toxicity profile of drug candidates in the initial phases of drug discovery.
QSAR is also a very popular tool for risk assessment of chemicals in the absence of experimental data.
Such approach is used by the United States Environmental Protection Agency and also encouraged in
the European Union’s REACH legislation. QSAR techniques are in consonance with the “3R concept”
related to the moral principle regarding the use of sentient animals. The Organization of Economic Cooperation and Development (OECD) has recommended a set of guidelines for development and validation
of QSAR models for regulatory uses. Apart from the conventional uses in drug discovery and predictive risk assessment, QSAR has also been used for modeling chemicals with special applications such
as antioxidants, ionic liquids, and nanomaterials. In the present day QSAR research, emphasis is given
on the (external) predictive quality and mechanistic interpretability of the models. However, it may be
remembered that the best predictive model may not have sufficient mechanistic interpretability, and on
the other hand, a model with very good interpretation may have low predictive ability. Thus, selection
of the best model depends on the objective of the analysis besides other factors.
QSARs and related chemometric tools are extensively used in different fields of chemistry with ap-
plications in medicine, environment, and agriculture for ranking of potential compounds for prioritizing
experiments. Starting in early 1960s with the Classical Hansch analysis and Free-Wilson model, QSAR
has come a long way with much advancement and modernization with respect to the descriptors and
validation tools used leading to evolution of a sophisticated scientific discipline on its own at the interface
of chemistry, biology, and statistics.
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Preface
This book is intended to showcase the recent developments in the field of QSAR with its applications in different emerging fields. There are a total of 15 chapters on different topics contributed by the
experts working in the field.
Before knowing the applications of QSAR in different fields of chemical research, the novice readers
should have some knowledge on the preliminaries of QSAR tools. QSAR essentially being a statistical
approach, a working knowledge of statistics is necessary for the development, validation, and interpretation of QSAR models. The first chapter entitled “An Introduction to the Basic Concepts in QSARAided Drug Design” and contributed by Maryam Hamzeh-Mivehroud, Babak Sokouti, and Siavoush
Dastmalchi covers the basic principles and tools used in QSAR. The authors give the background and
different applications of QSAR followed by the introduction of molecular structure representation and
the concept of descriptors. Then they discuss different descriptors and linear and non-linear statistical
tools used in QSAR studies. Different validation strategies used in QSAR are also mentioned. The authors
finally discuss the 3D-QSAR studies and higher dimensional QSARs. At the end, a few case studies
are presented and future directions are indicated. This chapter should be a good starting point for those
who are new to the field of QSAR.
Descriptors are fundamental to the development of QSAR models. They encode chemical information
in a numerical form making it suitable for statistical operations. Though there are hundreds of descriptors
present for development of QSAR models, there is always a need to develop novel descriptors that may
address some of the deficiencies of the previous ones. Moreover, choice of a particular type of descriptor depends on the structural complexity present in the data set and the type of response being modeled.
The second chapter entitled “The ‘ETA’ Indices in QSAR/QSPR/QSTR Research” contributed by Kunal
Roy and Rudra Narayan Das presents the algorithm of the novel Extended Topochemical Atom (ETA)
indices and their application in quantitative structure-activity/property/toxicity relationship studies. The
authors describe different kinds of descriptors that are in use in QSAR studies and then indicate the
advantages of two-dimensional descriptors. Then the evolution of the ETA descriptors is discussed, and
the definitions of different variants of first and second generations of the ETA indices are elaborated.
The authors also show sample calculation of the ETA indices for two representative molecules. The
available software tools for the computation of the ETA indices are mentioned, and finally, applications
of these indices in the modeling studies are discussed.
Multivariate Image Analysis (MIA) descriptors for QSAR are discussed in Chapter 3 (“Evolution of
Multivariate Image Analysis in QSAR: The Case for a Neglected Disease”) contributed by Matheus P.
Freitas and Mariene H. Duarte. The authors explain the basic prociples of MIA-QSAR and augmented
MIA-QSAR. They apply this approach for modeling a data set of compounds active against a neglected
disease (trypanosomiasis). The future prospects of this approach are also discussed.
The fourth chapter entitled “Quantitative Structure-Activity/Property/Toxicity Relationships through
Conceptual Density Functional Theory-Based Reactivity Descriptors” and contributed by Sudip Pan,
Ashutosh Gupta, Venkatesan Subramanian, and Pratim K. Chattaraj discusses implementation of Conceptual Density Functional Theory (CDFT)-based descriptors in quantitative structure-activity/property/
toxicity studies. The authors mention different types of descriptors that are used in QSAR analyses.
Then they discuss some descriptors defined based on the CDFT. Examples are cited for the use of the
CDFT-based descriptors in various modeling studies.
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xix