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10.3.2 Acid–base dissociation constant (pK
a
) 225
10.3.3 Bioavailability (F) 227
10.4 Distribution parameters of the drug molecule 229
10.4.1 Apparent volume of distribution (V
d
) 229
10.4.2 Plasma protein binding 230
10.4.3 CNS MPO score for the distribution of the drugs in the brain 231
10.4.4 Blood–brain barrier (BBB) score 232
10.5 Metabolism parameters of the drug molecule 233
10.6 Excretion parameters of the drug molecule 234
10.6.1 Clearance (CL) of the drug 235
10.6.2 Plasma half-life of the drug (t
1/2
) 236
10.7 Toxicity aspects of the drug molecule 237
10.8 Drug-likeness profile of the newly proposed drug molecule 240
10.8.1 Lipinski’s rule of five 241
10.8.2 Muegge (Bayer) filter of drug-likeness 241
References 244
Chapter 11
De novo drug designing approaches 248
11.1 Introduction 248
11.2 Classification of de novo drug designing 251
11.2.1 Structure-based de novo drug designing 251
11.2.2 Ligand-based de novo drug designing approach 262
11.3 Advantages of de novo drug designing approaches 265
11.4 Limitations of de novo drug designing approaches 266
References 267
Chapter 12
Homology modeling 269
12.1 Introduction 269
12.2 Structure of a protein 270
12.2.1 Primary structure of the protein 271
12.2.2 Secondary structure of the protein 271
12.2.3 Tertiary structure of the protein 272
12.2.4 Quaternary structure of the protein 273
12.3 Steps involved in the process of homology modeling 274
12.3.1 Identification of the sequence of the amino acids of the query
structure protein 274
12.3.2 Identification of the template protein 276
12.3.3 Alignment of the query protein with template protein structures 277
12.3.4 Building the model of the query protein 277
12.3.5 Refinement of the model developed 283
Contents XI
12.3.6 Assessment and validation of the developed model 287
12.4 Applications of homology modeling in CADD 289
12.5 Advantages of homology modeling 290
12.6 Limitations of homology modeling 290
References 291
Chapter 13
Pharmacophore mapping in computer-aided drug designing (CADD) 293
13.1 Introduction 293
13.2 Historical perspective of the pharmacophore concept 294
13.3 Understanding the concept: pharmacophore 296
13.4 Types of pharmacophore modeling (mapping) 297
13.4.1 Ligand-based pharmacophore modeling 297
13.4.2 Structure-based pharmacophore modeling 306
13.5 Applications of the pharmacophore modeling approaches 312
References 313
Chapter 14
Virtual screening approaches in the computer-aided drug designing 316
14.1 Introduction 316
14.2 Chemical databases for the virtual screening in CADD 316
14.3 Classification of virtual screening 318
14.3.1 Structure-based virtual screening 319
14.3.2 Ligand-based virtual screening 324
References 334
Index 337
XII Contents
Chapter 1
Introduction to computer-aided drug design
(CADD)
The power of computing has brought a new era in drug discovery, where the impossible is be-
coming routine through Computer-Aided Drug Design. – Eric Schmidt
1.1 Introduction
The discovery and the development of new drugs are critical endeavors in modern medi-
cine, aiming to address a wide range of health conditions and improve the quality of life
for millions of people worldwide [1–3].Traditionally,drugdiscoveryhasbeenalaborious
and expensive process, often taking years or even decades to identify and optimize poten-
tial therapeutic compounds [4]. However, in recent years, the field of computer-aided
drug design (CADD) has emerged as a powerful and transformative approach, revolution-
izing the drug discovery process. CADD, also known as computer-assisted drug design or
in silico drug design, involves the use of computational tools and techniques to expedite
andenhancevariousstagesofdrugdevelopment [5, 6]. By leveraging the computational
power of modern computers and algorithms, researchers can analyze molecular interac-
tions, predict biological activity, and design novel drug candidates more efficiently than
ever before.
The roots of CADD can be traced back to the late 1960s and early 1970s when re-
searchers first started using computers to model molecular structures and analyze
chemical interactions [7]. Early efforts focused on simple computational methods to
predict molecular properties, but with the rapid advancement of computing technol-
ogy, CADD techniques became increasingly sophisticated. While CADD has made sig-
nificant strides in accelerating drug discovery, challenges remain [8, 9]. Accurate
modeling of complex molecular interactions, accounting for solvent effects, and pre-
dicting drug metabolism are some of the ongoing research areas. In the future, advan-
ces in artificial intelligence (AI), machine learning, and quantum computing hold
great promise for further enhancing CADD capabilities. These technologies may en-
able more accurate predictions of binding affinities, faster virtual screening, and the
exploration of larger chemical spaces [10].
CADD has revolutionized the way new drugs are discovered and optimized. By
harnessing the power of computational tools and algorithms, researchers can effi-
ciently navigate the vast chemical space, identify promising drug candidates, and
bring innovative therapies to patients faster and at reduced costs. As technology con-
tinues to evolve, the impact of CADD on drug discovery is bound to grow, leading to
improved treatments for a wide range of diseases and medical conditions [11].
https://doi.org/10.1515/9783111434858-001
To understand the CADD process from the basics, it is first indispensable to have
a glimpse of the complete drug discovery process.
1.2 Drug discovery process
As per the considerations of the United States Food and Drug Administration (USFDA),
the drug development process is broadly divided into five phases [12, 13] (Fig. 1.1).
1.2.1 Discovery and development phase
The very first step in this phase is “target identification,” which includes finding the
target disease against whi ch a new drug is to be discovered. This is followed by the
search for potential active compounds against the targeted disease. These active com-
pounds, also known as hits, are obtained either by high-throughput screening (HTS)
or by virtual screening techniques (applied through any CADD methodology). Starting
from thousands of hits, very few are selected for further study, which have most of
the desired properties considered essential for therapeutic potential. The selected hits
are known as leads. In the development phase, the leads obtained from hits are sub-
jected to further studies for evaluating their absorption, distribution, metabolism, ex-
cretion, and toxicity (ADMET) parameters. Possible drug interactions are also studied
in this phase. These leads are modified further as per the requirement of potent drugs
against the targeted disease. In connection with the CADD approach, this is the most
crucial phase as most of the CADD approaches are applied in this phase of the drug
discovery [14].
1.2.2 Preclinical studies
Preclinical studies are conducted for the evaluation of safety, toxicity, potency, effi-
cacy, and dosing of the potential leads before the introduction of the drug to humans.
Preclinical studies include various in vitro and in vivo experiments, which are known
to play a pivotal role in deciding whether the drug under investigation could be thera-
peutically effective and safe for being administered to humans during clinical trials
[15]. In vitro and in vivo studies performed in the preclinical studies can be made hy-
brid by acc ompanying the same with the CADD methodologies such as in silico
ADMET predictions.
2 Chapter 1 Introduction to computer-aided drug design (CADD)
Fig. 1.1: Overview of the complete drug discovery process – from lead identification to post-market surveillance of the drug.
1.2 Drug discovery process 3
1.2.3 Clinical trials
Clinical trials are the longest phase of the drug development process and are carried
out on human beings after taking approval from the FDA and following highly regu-
lated and well-organized guidelines. The drug in this phase is now termed an Investi-
gational New Drug (IND). Clinical trials of IND are divided further into four phases
[16–19].
Phase I clinical trials are carried out on 20–100 healthy human volunteers. The
purpose of this study is to assess the safety and efficacy of IND in healthy human sub-
jects. Researchers carefully study different dosages of the drug to identify the optimal
dose that is both effective and safe. The focus is primarily on understanding how the
drug is metabolized, its pharmacokinetics (how the body processes the drug), and any
potential side effects or adverse reactions. Phase 1 trials are relatively short-term,
often lasting several months, but can vary, depending on the complexity of the drug
and the study objectives. Success in phase I is based on achieving the primary objec-
tive of determining the safety and tolerability of the investigational drug. Positive
findings at this stage pave the way for further testing in subsequent phases. Approxi-
mately, 70% of the IND passes to phase II of the clinical trial from phase I.
Phase II clinical trials are designed to further evaluate the safety and effective-
ness of the IND in a larger group of patients with the targeted medical condition or
disease. Phase II trials usually involve several hundred patients and can last for sev-
eral months to a few years, depending on the nature of the condition being studied
and the complexity of the drug’s mechanism of action. These trials are often random-
ized and controlled, meaning that some patients receive the investigational drug
while others receive either a placebo or the current standard care. The prime objec-
tive of phase II clinical trials is to ensure the safety and efficacy of the IND in patients,
along with collecting relevant data for dose optimization and rare side effects associ-
ated with the IND. This is the first time that IND is introduced to the patients with the
target disease condition and the success rate of the IND clearing this phase is approxi-
mately 33%.
Phase III clinical trials last from 1 to 4 years and involves 300 to 3,000 patients
with the disease condition. These trials occur after a potential treatment has shown
promising results in phase I and phase II trials. The main objective of phase III trials
is to gather additional data on the treatment’s safety, efficacy, and effectiveness in a
larger and more diverse population. The sample size of phase III is larger than in the
previous phases of clinical trials. In many phase III trials, neither the participants nor
the researchers know who is receiving the experimental treatment or the placebo.
This double-blind design helps minim ize bias and ensures that the results are not
influenced by expectations. The effectiveness of the new treatment is usually com-
pared against the control group to assess its superiority, non-inferiority, or equiva-
lence. Phase III trials are usually conducted at multiple research sites to increase the
diversity of participants and improve the generalizability of the results. This phase of
4 Chapter 1 Introduction to computer-aided drug design (CADD)
clinical trials has a success rate of approximately 25–30%. These trials can take sev-
eral years to complete and are often the final step before a treatment can be approved
and made available to the public for widespread use.
Phase IV clinical trial is the last phase of clinical trials and is carried out on a
large pool of thousands of patients. The main objective of this is to evaluate the long-
term efficacy and adverse effects of the drug. After the IND successfully completes the
previous three phases of the clinical trials, the researcher files a New Drug Applica-
tion (NDA) with the FDA. NDA provides complete data on the drug such as its safety,
dosing, and adverse reactions. After receiving the NDA, the FDA reviews the complete
application, and if it finds it satisfactory, it approves the NDA. Phase IV clinical trials,
also known as post-marketing surveillance trials or post-approval studies, are con-
ducted after a medication or medical device has been approved and is available on
the market. These trials are designed to gather additional information about the
safety, efficacy, and long-term effects of the intervention in a larger and more diverse
patient population than what was studied in the previous phases. Phase IV trials are
often conducted in real-wo rld settings, reflecting the everyday use of the treatment.
This trial provides valuable insights into how the treatment performs in a broader
patient population, including those with coexisting health conditions and those taking
other medications. Data from phase IV trials can lead to updates in the drug’s labeling,
including new indications, warnings, precautions, or dosing guidelines, based on the
additional information gathered.
It is noteworthy that a traditional drug discovery process takes around 15–20
years for the development of a new drug moiety against any specific disease. We start
from millions of molecules as our potential hits against a specific disease and we end
up with one or two molecules that emerge as successful drug candidates. To reduce
this time of drug discovery, wastage of resources, and to enhance the accuracy of
drug discovery, CADD has become a vital part of the drug discovery process. Techni-
ques of CADD can be applied at any stage of the drug development process, especially
from target identification to the point that drug enters a clinical trial process.
In an interesting study of 2003, search for novel transforming growth factor-β1
receptor kinase inhibitors was initiated. One group of scientists from Eli-lily employed
traditional drug discovery methods such as HTS, structur–acti vity relationship, and
in vitro assay, while another group of scientists from Biogen Idec employed the CADD
approaches such as virtual screening of the molecules and molecular docking. To
their immense surprise, the lead developed by the Biogen scientists through the CADD
approach was similar to that developed by Eli-Lilly, saving significant amount of cru-
cial time and resources.
We now have understood the importance of the CADD in any drug discovery pro-
cess – it is highly essential for reducing the time and cost of new drug development
without compromising the accuracy and precision of the whole process.
1.2 Drug discovery process 5
1.3 Computer-aided drug design (CADD)
Computer-aided drug design is a broad term and, in the simplest context, it can be
defined as any computational or in silico approach that is employed at any stage of
the complete drug discovery proces s [4, 5]. Nowadays, there are several techniques
available under the CADD, and all these techniques are broadly classified into two cat-
egories: structure-based drug designing (SBDD) and ligand-based drug designing
(LBDD) (Fig. 1.2). To know and study the various techniques of CADD, a new field of
medicinal chemistry has now been gaining immense popularity, which is also known
as pharmacoinformatics.
1.3.1 Structure-based drug designing (SBDD)
SBDD is the approach of drug designing in which the structure of our target receptor
is known and all our designing of drugs revolves around the structure of the receptor
[20]. Examples of SBDD include molecular docking, molecular dynamics, and pharma-
cophore mapping:
– Molecular docking: Molecular docking is a widely used technique that simulates
the interaction between a ligand (small molecule) and a receptor (protein) to pre-
dict the binding mode and affinity. This helps identify potential lead compounds
for further optimization.
– Molecular dynamics simulation: Molecular dynamics simulations provide insights
into the dynamic behavior of biomolecules, allowing researchers to study protein–
ligand interactions over time and understand the stability of the complexes.
– Pharmacophore modeling: Pharmacophore models represent the essential fea-
tures necessary for a ligand to interact with a target. This aids in virtual screening
and the identification of molecules that fit the target’s binding site.
1.3.2 Ligand-based drug designing (LBDD)
LBDD is the approach of CADD in which we have a set of known compounds against a
particular disease, reported in the literature, and the designing approach of new li-
gands is based on the structural properties of these known compounds. Examples of
LBDD include 2D-QSAR, 3D-QSAR, and ligand-based pharmacophore mapping:
– Quantitative structure–activity relationship (QSAR): QSAR models correlate the
chemical structure of molecules with their biological activity [21, 22]. These mod-
els can predict the activity of new compounds based on their structural features,
guiding lead optimization efforts. Based on the structural properties of the com-
pounds, these QSAR methodologies are further classified into 2D-QSAR, 3D-QSAR,
and so on.
6 Chapter 1 Introduction to computer-aided drug design (CADD)
Fig. 1.2: Classification of computer-aided drug designing (CADD) into structure- and ligand-based drug
designing.
1.3 Computer-aided drug design (CADD) 7
To be well-versed in the knowledge of CADD and to apply its principles, it is required
that one must study the subject from the basics, starting from the historical develop-
ment of CADD.
1.4 Historical perspective of CADD
The CADD in the twenty-first century has come a long way and many sophisticated
approaches have now come into existence for aiding the drug discovery process. But
to trace back its history, we have to go back to the early 1960s when the CADD first
come into existence.
CADD in the 1960s: This decade is dedicated to the development of modern QSAR. It
was Corwin Hansch and Toshio Fujita who first developed the correlation of biologi-
cal activity with the structural properties of molecules, which laid the foundation of
QSAR in 1964 [23]. Another breakthrough in this decade was the Free-Wilson approach
developed by Spencer Free Jr. and James W. Wilson in 1964, which laid the foundation
of the modern 2-D QSAR approach that is still used and is relevant worldwide [24].
Another milestone of this decade is the foundation of the Cambridge Crystallographic
Data Centre by Dr. Olga Kennard in 1965, which collects bibliographic, chemical, and
crystal structure data for small molecules and encodes it electronically. Later, it be-
came the Chemical Structure Database [25].
CADD in the 1970s: This decade began with the establishment of the Protein Data
Bank (PDB) in 1971 at the Brookhaven National Laboratory under the dynamic leader-
ship of Walter Hamilton. Started with only 7 structures, PDB currently has more than
2,00,000 structures of proteins that are required for many CADD approaches, like mo-
lecular docking and molecular dynamics. This decade has also seen the expansion of
QSAR to environmental and toxicology studies [26].
CADD in the 1980s: The 1980s can be considered the decade of the SBDD evolution.
The world’s first nucleotide sequence database, the EMBL Nucleotide Sequence Data
Library, was established in 1980 in Germany [27]. The first molecu lar docking pro-
gram “UCSF Dock” was published in the year 1982 [28]. The concept of molecular me-
chanics came into existence and this decade has seen the emergence of many force
fields such as AMBER, CHARMM, and MOPAC. In the last years of the decade, we have
seen the development of other popular molecular docking software like AutoDock.
This decade also witnessed immense development in the field of X-ray crystallography
and NMR spectroscopy for determining protein structures. The first linear 3D-QSAR
analysis, that is, comparative molecular field analysis (CoMFA) was introduced by
Cramer et al. in 1988 [29]. All these advancements paved the way for the modern
CADD that we are using today.
8 Chapter 1 Introduction to computer-aided drug design (CADD)
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