Table Of ContentBig Data Analytics in
Chemoinformatics and
Bioinformatics
Big Data Analytics in
Chemoinformatics and
Bioinformatics
With Applications to Computer-Aided
Drug Design, Cancer Biology, Emerging
Pathogens and Computational
Toxicology
Edited by
Subhash C. Basak
Department of Chemistry and Biochemistry,
University of Minnesota, Duluth, MN,
United States
Marjan Vracˇko
Theory Department, Kemijski insˇtitut/National
Institute of Chemistry, Ljubljana, Slovenia
Preface
“Weadorechaosbecausewelovetoproduceorder.”
—M.C.Escher
“...shallwestayourupwardcourse?InthatblessedregionofFourDimensions,
shallwelingeratthethresholdoftheFifth,andnotentertherein?Ah,no!Letus
ratherresolvethatourambitionshallsoarwithourcorporalascent.Then,yielding
toourintellectualonset,thegatesoftheSixDimensionshallflyopen;afterthata
Seventh,andthenanEighth...”
—EdwinAbbott,In:Flatland
“I’mtiredofsailingmylittleboat
Farinsideoftheharborbar;
Iwanttobeoutwherethebigshipsfloat—
OutonthedeepwheretheGreatOnesare!...
—DaisyRinehart
InsciencethereisandwillremainaPlatonicelementwhichcouldnotbetaken
awaywithoutruiningit.Amongtheinfinitediversityofsingularphenomena
sciencecanonlylookforinvariants.
—JacquesMonod
We are currently living in an age when many spheres of science and life are
flushed with the explosion of big data. We are familiar with the term “data is the
newoil,”butoftenhearaboutinformationoverload ordatadeluge. Weneedtosys-
tematically manage, model, interpret, visualize, and use such data in diverse
decision-support systems inbasic research, technology,health care,andbusiness,to
namejustafew.
If we look at the main focus of this book—applications of big data analytics in
chemoinformatics, bioinformatics, new drug discovery, and hazard assessment of
environmentalpollutants—,itisevidentthatdatainallthesefieldsareexploding.
Regarding the size of chemical space, the GDB-17 database contains 166.4 bil-
lion molecules containing up to 17 atoms of C, N, O, S, and halogens which fall
within the size range containing many drugs and are typical for druggable lead
compounds. The sequence data on DNA, RNA, and proteins are increasing each
day by new depositions by researchers worldwide. A simple combinatorial exercise
ofsequencepossibilityfora100-residuelongproteinsuggests20100differentpossi-
ble sequences (considering 20 frequently occurring natural amino acids). Modern
computer software can calculate many hundreds, sometimes thousands of
xx Preface
descriptors for a molecule or a macromolecular sequence. The Vs of big data, viz.,
validity, vulnerability, volatility, visualization, volume, value, velocity, variety,
veracity,andvariability,increasethecomplexityofbigdataanalyticsimmensely.
Here, we come face to face with the stark reality of the curse of dimensionality
in the big data space of chemistry and biology. Following the parsimony principle,
we need to be careful in feature selection and use of robust validation techniques in
model building. Finally, analysis and visualization of models to understand their
meaningandderive actionableknowledgefromthe vastinformation spacefor prac-
tical implementation in the decision-support systems of science and society are of
paramountimportance.
The first section, General Section, of the book has three chapters. Chapter 1
brieflytracesthehistoryofthedevelopmentofchemodescriptorsandbiodescriptors
spanning three centuries—from the eighteenth century to the present. It is observed
by the author that the initial characterization of structures, both chemical and bio-
logical,werequalitativewhichwasgraduallyfollowedbythedevelopmentofquan-
titative chemodescriptors and biodescriptors. The author concluded that in the
sociallyandeconomicallyimportantareasofnewdrugdiscoveryandhazardassess-
ment of chemicals use of a combined set of chemodescriptors and biodescriptors
for model building using big data would be a useful and practical paradigm.
Chapter 2 deals with the problem of robust model building from noisy high-
dimensional data, focusingprimarily on the robustness aspects against data contam-
ination. The author also demonstrates the utility of his method in the prediction of
salmonella mutagenicity of a set of amines, a class priority pollutants. Chapter 3
delves into the ethical issues associated with the landscape of desirable qualities
such as fairness, transparency, privacy, and robustness of currently used machine
learning(ML)methodsofbigdataanalysis.
The second section, Chemistry and Chemoinformatics Section, of the book has
nine chapters. Chapter 4 discusses the use of big data in the characterization of
adverse outcome pathways (AOPs), a novel paradigm in toxicology. The author
integrated “big data”—the(cid:1)omics and high-throughput (HT) screening data—to
derive AOPs for chemical carcinogens. Chapter 5 discusses the latest progress in
the use of ML and DL (deep learning) methods in creating systems that automati-
cally mine patterns and learn from data. The author also discuss the challenges and
usefulness of DL for quantitative structure(cid:1)activity relationship (QSAR) modeling.
Chapter 6 describes retrosynthetic planning and analysis of organic compounds in
the synthetic space using big data sets and in silico algorithms. Chapter 7 discusses
that the vast amount of historical chemical information is not only a rich source of
data, but also a useful tool for studying the evolution of chemistry, chemoinfor-
matics, and bioinformatics through a computational approach to the history of
chemistry. The author exemplifies that by a case study of recent results on the
computational analysis of the evolution of the chemical space. Chapter 8 gives a
detailed description of combinatorial techniques useful in studying large data sets
with hypercubes and halocarbons as the main focus. Quantum chemical techniques
discussed here can generate electronic parameters that have potential for use in
QSAR for toxicity prediction of big data sets. Chapter 9 deals with the use of
Preface xxi
computed high-level quantum chemical descriptors derived from the density func-
tional theory in the prediction of property/toxicity of chemicals. Chapter 10 covers
the important area of the use of computed pharmacophores in practical drug design
from analysis of large databases. Chapter 11 uses ML based classification methods
for the detection of hot spots in protein(cid:1)protein interactions and prediction of new
hotspots. Chapter 12 discusses applications of decision tree methods like recursive
partitioning, phylogenetic-like trees, multidomain classification, and fuzzy cluster-
ing within the context of small molecule drug discovery from analysis of large
databases.
The third section, Bioinformatics and Computatioanl Toxicology Section, of the
book has seven chapters. Chapter 13 discusses their contributions in the emerging
area of mathematical proteomics approach in developing biodescriptors for the
characterization of bioactivity and toxicity of drugs and pollutants. Chapter 14 dis-
cusses the important role of efficient computational frameworks developed to cata-
log and navigate the protein space to help the drug discovery process. Chapter 15
discusses applications of ML and DL approaches to HT sequencing data in the
developmentofprecision medicine usingsingle-nucleotidepolymorphismsasatool
of reference. Chapter 16 discusses the development and use of a new class of
sequence comparison methods based on alignment-free sequence descriptors in the
characterizationofemergingglobal pathogens likethe Zika virusandcoronaviruses
(SARS, MERS, and SARS-CoV-2). Chapter 17 discusses the important and emerg-
ing issue of different ways of building QSARs from large and diverse data sets that
can be continuously updated and expanded over time. The importance of modular-
ity in scalable QSAR system development is also discussed. Chapter 18 deals with
the applications of network analysisand bigdata tostudyinteractions of drugs with
their targets in the biological systems. The authors point out that a paradigm shift
integrating big data and complex network is needed to understand the expanding
universe of drug molecules, targets, and their interactions. Finally, Chapter 19
reports the use ofML approaches consistingof supervisedand unsupervised techni-
ques inthe analysis ofRNAsequence data ofbreast cancer toderive important bio-
logical insights.Theywere abletopinpointsomedisease-related genesandproteins
inthebreastcancernetwork.
Finally, we would like to speciallymention that indrug research and toxicology,
we are witnessing an explosion of data, which are expressed by four principal Vs—
volume, velocity, variety, and veracity. However, the data per se is useless, the real
challenge is the transition to the last two steps on the three-step path to knowledge:
data (cid:1) information (cid:1) knowledge. When we talk about big data in drug research
and toxicology, we often think of omics data and in vitro data derived from HT
screening. On the other hand, a pool of high-quality “small” data exists, which has
been collected in the past. Under the label “small data” we have the standard toxi-
cological data based on well-defined toxic effects. A future challenge for us is to
integratebothdataplatforms—bigandsmall—intoanewandintegratedknowledge
extractionsystem.
SubhashC.Basak
MarjanVracˇko
List of contributors
Anshika Agarwal In silico Research Laboratory, Eminent Biosciences, Indore,
MadhyaPradesh,India
Sarah Albogami Department of Biotechnology, College of Science, Taif
University,Taif,SaudiArabia
Nandadulal Bairagi Department of Mathematics, Centre for Mathematical
BiologyandEcology,JadavpurUniversity,Kolkata,West Bengal,India
Krishnan Balasubramanian School of Molecular Sciences, Arizona State
University,Tempe,AZ,UnitedStates
Subhash C. Basak Department of Chemistry and Biochemistry, University of
Minnesota,Duluth,MN,UnitedStates
Emilio Benfenati Laboratory of Environmental Chemistry and Toxicology, Istituto
diRicerche FarmacologicheMarioNegriIRCCS,Milano,Italy
Apurba K. Bhattacharjee Department of Microbiology and Immunology,
Biomedical Graduate Research Organization, School of Medicine, Georgetown
University,Washington,DC,UnitedStates
Anushka Bhrdwaj In silico Research Laboratory, Eminent Biosciences, Indore,
Madhya Pradesh, India; Department of Bioinformatics, Computer Aided Drug
Designing and Molecular Modeling Lab, Alagappa University, Karaikudi, Tamil
Nadu,India
SumanK.Chakravarti MultiCASEInc.,Beachwood,OH,UnitedStates
Pratim Kumar Chattaraj Department of Chemistry, Indian Institute of
TechnologyKharagpur,Kharagpur,WestBengal,India
Samrat Chatterjee Complex Analysis Group, Translational Health Science and
TechnologyInstitute,NCRBiotechScienceCluster,Faridabad,Haryana,India
xvi Listofcontributors
Ramana V. Davuluri Department of Preventive Medicine, Division of Health and
Biomedical Informatics, Northwestern University Feinberg School of Medicine,
Chicago,IL,UnitedStates
Tathagata Dey Centre for Interdisciplinary Research and Education, Kolkata,
West Bengal, India; Department of Computer Science & Engineering, Indian
InstituteofTechnologyBombay,Mumbai,Maharashtra,India
AbhikGhosh IndianStatisticalInstitute,Kolkata,WestBengal,India
Indira Ghosh School of Computational & Integrative Sciences, Jawaharlal Nehru
University,NewDelhi,Delhi,India
Giuseppina Gini Politecnico di Milano, DEIB, Piazza Leonardo da Vinci, Milano,
Italy
Lima Hazarika In silico Research Laboratory, Eminent Biosciences, Indore,
MadhyaPradesh,India
Guang Hu Department of Bioinformatics, Center for Systems Biology, School of
BiologyandBasicMedicalSciences,SoochowUniversity,Suzhou,P.R.China
Chiakang Hung Politecnico di Milano, DEIB, Piazza Leonardo da Vinci, Milano,
Italy
Tajamul Hussain Biochemistry Department, College of Science, King Saud
University, Riyadh, Saudi Arabia; Center of Excellence in Biotechnology Research,
CollegeofScience,KingSaudUniversity,Riyadh,SaudiArabia
Yanrong Ji Department of Preventive Medicine, Division of Health and
Biomedical Informatics, Northwestern University Feinberg School of Medicine,
Chicago,IL,UnitedStates
Isha Joshi In silico Research Laboratory, Eminent Biosciences, Indore, Madhya
Pradesh,India
Taushif Khan Immunology and Systems Biology Department, OPC-Sidra
Medicine,Ar-Rayyan,Doha,Qatar
Ravina Khandelwal In silico Research Laboratory, Eminent Biosciences, Indore,
MadhyaPradesh,India
Pawan Kumar National Institute of Immunology, Aruna Asaf Ali Marg, New
Delhi,Delhi,India
Listofcontributors xvii
Shivam Kumar Complex Analysis Group, Translational Health Science and
TechnologyInstitute,NCRBiotechScienceCluster,Faridabad,Haryana,India
Min Li Department of Bioinformatics, Center for Systems Biology, School of
BiologyandBasicMedicalSciences,SoochowUniversity,Suzhou,P.R.China
Jie Liao Department of Pathology, Northwestern University Feinberg School of
Medicine,Chicago,IL,UnitedStates
Claudiu N. Lungu Department of Chemistry, Faculty of Chemistry and
Chemical Engineering, Babes-Bolyai University, Cluj, Romania; Department
of Surgery, Faculty of Medicine and Pharmacy, University of Galati, Galati,
Romania
Subhabrata Majumdar AI Vulnerability Database, Seattle, WA, USA; Bias
Buccaneers,Seattle,WA,USA
Rama K. Mishra Department of Biochemistry and Molecular Genetics, Feinberg
SchoolofMedicine,NorthwesternUniversity,Chicago,IL,UnitedStates
Manju Mohan In silico Research Laboratory, Eminent Biosciences, Indore,
MadhyaPradesh,India
Ashesh Nandy Centre for Interdisciplinary Research and Education, Kolkata, West
Bengal,India
Anuraj Nayarisseri In silico Research Laboratory, Eminent Biosciences, Indore,
Madhya Pradesh, India; Department of Bioinformatics, Computer Aided Drug
Designing and Molecular Modeling Lab, Alagappa University, Karaikudi, Tamil
Nadu, India; Biochemistry Department, College of Science, King Saud University,
Riyadh, Saudi Arabia; Bioinformatics Research Laboratory, LeGene Biosciences
PvtLtd,Indore,MadhyaPradesh,India
ShahulH.Nilar GlobalBloodTherapeutics,SanFrancisco,CA,UnitedStates
Ranita Pal Advanced Technology Development Centre, Indian Institute of
TechnologyKharagpur,Kharagpur,WestBengal,India
Aditi Pande In silico Research Laboratory, Eminent Biosciences, Indore, Madhya
Pradesh,India
Guillermo Restrepo Max Planck Institute for Mathematics in the Sciences,
Leipzig, Germany; Interdisciplinary Center for Bioinformatics, Leipzig University,
Leipzig,Germany
