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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5691_Библиотеки_им_академика_М_И_Перельмана
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likeliness, thus helping in designing an appropriate molecule. A summary of applica-
tions is presented in Fig. 3.2.
3.4 Future challenges of AI
There is no doubt regarding the significant contribution AI can make in the process of
drug discovery as well as drug dose optimization and the management of huge data
on clinical efficacy. However, presently the field has a lot of potential but is under-
used, and there are no drugs currently developed using this technology. The challenge
is to optimize the approach for drug discovery and the entire health system for
human welfare.
3.5 Theoretical drug designing
The two major areas of medicinal chemistry are the exploration for leads and their
further exploitation for drug development. This includes discovering new drugs with
greater potency and less toxicity. Not only this but also molecular modification in ex-
isting drugs, synthesizing new molecules, and finding new constituents from natural
resources to develop new compounds suitable for drug development [4– 6]. The real
problem in medicinal chemistry is identifying a lead compound for a specific activity.
Thus, the prerequisite of drug design is recognizing the biological principle of drug
action and accordingly modulating the chemical molecules. For this, different struc-
tural, physiochemical and biological parameters should be used to correlate struc-
ture-activity relationships. This enables us to develop new molecules with desired or
optimum activity. Biological activity is a consequence of different interactions of a
biomolecule or substance that happens at a particular site in the biological system
Fig. 3.2: AI in drug discovery.
3.5 Theoretical drug designing 41
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through simultaneous complex reactions involved in the pharmacokinetics of the bio-
molecule. Drug molecules have to face different uncertainties like absorption, trans-
port, metabolism, excretion and an uncertain path to the critical reaction site, not to
mention finally the toxicity. This has to be addressed in view of the biological aspect
of disease vis-à-vis drug action. Thus, a very thorough understanding of the target site
with a view of the influence of structure and physicochemical properties of drugs on
a particular activity has to be the fundamental basis of the so-called rational drug de-
sign. The onus of theoretical drug design, thus, is a very complex process, and these
processes are key factors for a successful strategy of drug design. Although computers
and advanced software have come to the rescue, vast development in this area has
truly revolutionized the entire gamut of the drug design area.
3.6 Computer-aided drug design (CADD)
Computer-aided drug design (CADD) offers a set of tools and techniques that assist in
drug design. This not only reduces the cost of drug research but also helps in the de-
velopment of better drugs. Since drug discovery involves a lot of cost, is very complex
and time-consuming, the CADD approach has been widely used in the pharmaceutical
industry. Not only this, drug design and optimization involve using computers for vir-
tual screening, DNA microarray expe riments and finding theoretical pathways and
toxicity of drugs. The tools use empiri cal molecular mechanics, quantum mechanics
as well as statistical mechanics to facilitate the drug discovery process, thanks to the
availability of high-performance computer graphics. Since the present book concerns
metal-based drugs and metal complexes as therapeutics, only a summary of tools like
CADD is being given (Fig. 3.3).
Fig. 3.3: CADD process: a flow sheet.
42 Chapter 3 Theoretical drug designing and sustainable medicine: a new paradigm
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3.7 Available tools for theoretical drug design
Some of the tools being used in recent years for the success ful designing of a drug
include:
1. Prediction of activity spectra of substances [7]
2. Absorption, distribution, metabolism, excretion (ADME) and toxicity profiling [8]
3. Molecular docking [9]
3.7.1 Prediction of activity spectra of substance (PASS)
On the basis of the structure of a compound, PASS predicts over 4,000 kinds of biologi-
cal activities, including pharmacological effects, mechanisms of action, toxic and ad-
verse effects, interactions with enzymes involved in metabolism, transport, influence
on gene expression, etc. The activity spectra are available online, making it an ex-
tremely useful tool. This does not require the synthesis of a molecule since it can pre-
dict the profile of any virtual molecule, which may be synthesized accordingly. Thus,
over 4,000 kinds of activities are predicted beforehand with an accuracy of ~95%. This
is done only using the structural formula. Thus, the application of PASS is wonderful
in theoretically designing any drug since it also predicts the probable mechanism of
action. On this basis, the most promising compounds can be selected for further high-
throughput screening, and finally, the most relevant screens for particular com-
pounds are theoretically found. Thus, in all, it seems very simple, and most impor-
tantly, the success rate onl y depends on a number of factors. Not going much into
detail, only an introduction to various terms is expla ined below. Any reader inter-
ested in more details can consult relevant literature:
– Pa – it is the probability of being active. It predicts the chances that the com-
pound studied belongs to the subclass of active compounds. It means how the
structure resembles the structure of molecules that are most typical in a sub set
of activities in the training set.
– Pi describes the inactivity of the compounds.
In general, a Pa value of more than 0.3 (30%) may not possess the predicted activity,
whereas a value of 0.7 or more predicts the chances of the molecule being a drug can-
didate. Thus, values above 0.3 can be experimentally validated. The application of this
tool can be seen in another book by the authors [10] as well as in Chapter 5.
3.7 Available tools for theoretical drug design 43
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3.7.2 The absorption, distribution, metabolism, excretion (ADME) and toxicity
profiling
Poor pharmacokinetics and toxicity are two important causes of failure in drug devel-
opment. These two are as thus need to be considered a priority in the area of drug
discovery. On the other hand, the development of advanced combinatorial chemistry
and high-throughput screens has enormously increased the number of compounds
for which such data are needed before furthering the discovery process. Thus, these
problems need to be addressed in the area of theoretical drug designing.
In short, A DMET has significantly addressed the problem of drug discovery. A
precise strategy in this area for the testing of any drug before evaluation at the clini-
cal stage is addressed by ADMET. This includes testing the drug’s metabolism, phar-
macokinetics and even toxicity. This has expedited the time-consuming process and
greatly helped combinatorial chemists.
Assessment of the pharmacokinetics or pharmacological properties of absorption,
distribution, metabolism and excretion, as well as the toxicity of a candidate molecule
or lead, [5] is critical to their initial selection. This establishes a benchmark for the
synthetic plan of a molecule. Moreover, optimization can be strengthened by modulat-
ing or improving the ADME properties during the process of drug discovery. Improve-
ment in ADME properties preserves the potency and selectivity of the molecules and
may lead to the discovery of more efficient drug molecules. This book is dedicated to
metals and metal complexes for therapeutic a pplications; it would be beyond the
scope to go into more details of theoretical drug design. However, Tab. 3.1 is included
for ADMET software sources so that any reader interested may consult them.
Tab. 3.1: ADMET softwares commonly available.
S. no. Product Company/institute/academics/consultants Websites
. PASS V. Poroikov www.timetec.net/Software/
pass.htm
. Hybot,
Slipper
O.A. Raevsky www.ibmh.mskSu/mol pro/
. Mol Surf P. Sjoberg University of Washington qemist@swipnet.se
. Molconn_Z Hall Associates Consulting Reckon.data@global.net
. Paragon M.G. Ford Centre for Molecular Design, University of
Portsmouth, UK
www.cmd.Port.ac.uk/cmd/soft
ware.shtml
44 Chapter 3 Theoretical drug designing and sustainable medicine: a new paradigm
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3.7.3 Molecular docking: a powerful tool for structure-based drug discovery
In recent years, molecular docking has emerged as an increasingly important tool for
drug discovery research. After the completion of the Human Genome Project, an in-
creasing number of new therapeutic targets have been identified. This has opened up
enormous applications for structure-based drug discovery, and the docking approach
is one of them. High-throughput protein purification and sophisticated characteriza-
tion techniques like crystallography and NMR have further contributed to the struc-
tural details of not only proteins but also protein-ligand complexes. In sum, CADD has
greatly benefited from these advancements [11, 12]. Thus, as far as structure-based
drug design is concerned, molecular docking is a widely used method. A number of
reviews have been published in the recent years [13–15]. The field of molecular model-
ing is significantly impacted by molecular docking. This is a method for predicting the
preferred orientations of one molecule to a second one when a ligand and target are
binding to each other. These orientation issues and results are used to predict the
strength of association between ligand and target molecules in the form of a scoring
function.
Signal transduction is the transmission of molecular signals from outside the cell
into the cell via cell surface receptors. This is important since identifying the cellular
events that take place upon receptor activation is necessary for designing pharmaco-
logical agents that potentiate or diminish signaling. The associations between bioac-
tive substances or biologically relevant molecules like pep tides, proteins, nucleic
acids, carbohydrates or even lipids play a vital role in signal transduction. Moreover,
the relative orientation of the ligand and target may affect the type of signal pro-
duced, i.e., agonistic or antagonistic. Molecular docking is one such frequently used
method in structure-based drug design. It predicts the binding conformation of small
molecules (ligands) to the appropriate target site. The binding behavior plays a signifi-
cant role in the rational design of drugs and also elucidates the biochemical processes
involved. Before a brief explanation of docking, it seems appropriate to make the
reader conversant with the glossary of docking:
(a) Docking: This is the computational simulation of a candidate ligand binding to a
receptor.
(b) Receptor or lock or host: It is the receiving molecule, mostly a protein or an-
other such biopolymer.
(c) Ligand or guest or key: This is a complementary partner molecule binding to a
receptor. These are mostly small molecules or other biopolymers.
(d) Binding mode: This is, in general, the orientation of the ligand relative to the re-
ceptor. It is also the conformation of the ligand and receptor when bound to each
other.
(e) Scoring: The process of evaluating a particular pose by counting the number of
favorable intermolecular interactions such as hydrogen bonds and hydrophobic
contacts.
3.7 Available tools for theoretical drug design 45
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(f) Pose: A candidate’s binding mode.
(g) Ranking: Based on the free energy of binding, the process of classifying which
ligands are most likely to interact favorably with a particular receptor.
(h) Docking assessment (DA): A procedure to quantify the predictive capability of a
docking protocol.
A brief explanation of molecular docking can be visualized as a problem of lock and
key, where we try to find the correct relative orientation of the key that opens a lock.
The protein here is thought of as the lock, and the ligand is the key. Thus, molecular
docking describes the “best-fit” orientation of any ligand that binds to a particular
protein of interest. The only difference from this is the lock. The key analogy is the
flexibility of ligands and proteins. In this sense, the “hand-in-glove” simile is more apt
[16] compared to the lock-and-key one. The docking process involves adjust ing their
conformation to achieve an overall “best-fit” one, and this adjustment resulting in
overall binding is called “induced-fit” [17]. Thus, molecular docking is, in fact, compu-
tationally simulating the molecular recognition process to achieve an optimized con-
formation for both the protein and ligand. Moreover, the relat ive orientation of the
protein and ligand is suc h that the free energy of the overall system is minimized.
Figure 3.4 shows a simplified image of docking.
There are two approaches to molecular docking:
1. Matching technique describing protein and ligand as complementary surfaces
[18–20].
2. Simulating the actual docking process in which the ligand-protein pairwise inter-
action energies are calculated [21].
Shape complementary methods use the protein and ligand as a set of features, making
them worth docking. These features include molecular surface descriptors. The recep-
tor’s molecular surface is described in terms of solvent-accessible surface area, whereas
the ligand’s molecular surface is treated in terms of its matching surface description.
Thus, their complementarity amounts to the shape-matching description that helps in
finding the complementary pose of docking the target and ligand molecules. Another
approach is describing hydrophobic features of the protein using turns in the main-
chain atoms.
Fig. 3.4: Target ligand complex.
46 Chapter 3 Theoretical drug designing and sustainable medicine: a new paradigm
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To perform docking, the foremost requirement is the structure of the protein of
interest. Understandably, the structure has already been determined using X-ray crys-
tallography, NMR or cryoelectron microscopy techniques. These are called biophysical
techniques. The structures can also be determined using homology modeling. Thus,
for potential ligands, the structures determined by either of the methods serve as da-
tabase input. However, the success of any docking relies on the search algorithm and
the scoring function.
Search algorithms theoretically work out all possible orientations and conformations
of the protein paired with the ligand. However, practically, it is almost impossible to ex-
haustively explore the search space due to the limitations of available computational re-
sources. This involves working out all possible distortions of each molecule and all
possible rotational as well as translational orientations of the ligand with respect to the
protein. In general, most docking programs work using the whole conformational space
of the ligand, i.e., the flexibility of a ligand. Each such “snapshot” of the pair is referred to
as a pose. In view of this, the conformation of any ligand is generated in the absence of
the receptor and subsequently docked. Alternatively, the conformation may be generated
on-the-fly in the presence of the receptor binding cavity. It may even be done with full
rotational flexibility of each dihedral angle, taking into account the accurate fragment-
based docking [22, 23]. However, force field energy evaluation is commonly used to select
energetically reasonable conformations [24]. Sometimes, knowledge-based methods are
also used. Further, multiple static structures, which are experimentally determined for
the same protein in different conformations, are commonly used to emulate receptor
flexibility. Finally, docking programs generate a large number of potential ligand poses,
out of which some can be rejected due to clashes with proteins. Those that are left are
evaluated using a scoring function that takes a pose as input and results in a number
showing the likelihood that the pose represents a favorable binding interaction for a
comparison of one ligand relative to another.
Scoring functions are physics-based molecular mechanics force fields estimating
the energy of any pose within the binding site. They can be written as an equation:
ΔG
bind
= ΔG
solvent
+ ΔG
conf
+ ΔG
int
+ ΔG
rot
+ ΔG
t=t
+ ΔG
vib
A low (negative) energy indicates a stable system and thus is a likely binding interaction.
Coming to the actual application, conclusively docking funds the binding interac-
tion between a small molecule ligand and an enzyme protein, which may activate or
inhibit the enzyme. If the protein is a receptor, the ligand binding may result in either
agonism or antagonism. So, the docking is applied for the following purposes:
1. Docking combined with a scoring function is used to find potential drugs, but vir-
tual screening is used to identify molecules likely to bind to a protein in silico.
2. Lead optimization: Docking is used to predict where and in which relative orien-
tation a ligand binds to a protein. This is called the binding pose.
3.7 Available tools for theoretical drug design 47
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3. It can also be of use for bioremediation purposes. However, back bone flexibility
and movements of many secondary elements of the receptor involving ligand bind-
ing or even catalysis are major obstacles in docking scoring functions, which is a
fundamental component that still needs improvement. Further, the realistic inter-
actions between small molecules and receptors rely largely on experimental meth-
odologies. Finally, accurate and low-cost or rather cost-effective computational
methods will bring docking applications to a new vista. Lastly, a brief summary of
docking, Fig 3.5 is given to make the reader conversant with this important and
significant tool of drug discovery.
3.7.4 Swiss ADME: a smart method for small molecules
Any potent molecule, if it is supposed to be effective as a drug, must reach its target in
the body in proper concentration and remain there in a bioactive form for sufficient
time for the biological event or activity to occur. Thus, drug development is associated
with the assessment of absorption, distribution, metabolism and excretion, abbrevi-
ated as ADME, and finally toxicity. The new SWISS ADME is a web tool giving free
access to predictive models for physicochemical properties, phar macokinetics and
drug-likeness and is thus an extremely helpful aid for medicinal chemistry. The
SWISS ADME web tool is freely accessible at http://www.swissadme.ch/ and is an ex-
tremely user-friendly tool for submitting and analyzing results. It is useful for both
experts and nonexperts involved in CADD. Since this is an introductory part, anyone
interested in details may consult the relevant literature.
Table 3.2 lists some software available for structure-based drug designing.
Molecular docking
Search algorithm
Systematic
molecular
dynamics
Local shape
features matching
Genetic
algorithm
Screening function
Force
field
Empirical Knowlege
based
Docking
accuracy
Enrichment
factor
Prospective
pharmacological
validation
Docking assesment
Fig. 3.5: Flowchart: summary of docking mechanics.
48 Chapter 3 Theoretical drug designing and sustainable medicine: a new paradigm
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3.8 Summary
Theoretical drug designing, or CADD, can be very useful when combined with experi-
mental validation for accelerating the drug discovery process. This is a cost-effective,
fast and a priori tool that does not require any synthesis. It minimizes testing and syn-
thetic protocols and screens unnecessary efforts. To sum up, this is going to be the
future of drug discovery.
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Tab. 3.2: List of softwares for computer aided drug designing.
Function Tools Remarks Websites
Target
modeling
SWISS ADME Homology modeling https://www.swissadme.ch/
Modeler Homology modeling https:/sallab.org/modeler
Phyre and
Phyre
Template detection, alignment,
as well as D modeling
http://www.sbg.bio.ic.ac.uk/phyre/
html/page cgi id=index
Binding sites
prediction
CASTP Prediction of binding site http:/Sts.bioe.uic.edu/castp/index.
html
Active site
prediction
Active site
prediction tool
Prediction of active site http://www.scfbio-td.res.in/dock/ac
tive site
Molecular
docking
Auto Dock Vina Molecular docking and virtual
screening
https://www.vina.scripps.edu/
Schrödinger Maestro https://www.Schrodingercom/prod
ucts/maestro
References 49
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50 Chapter 3 Theoretical drug designing and sustainable medicine: a new paradigm
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