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sumed by the MLR simple regression approach. Because MLR uses stepwise regression to
identify best-suited model, for large number of descriptors, it may be time-consuming.
Such a flaw is addressed by PCA, which may reduce data from huge number of variables
to a lesser group of distinctive variables. The trouble in obtaining particulars of chemical
descriptors which contribute toward the pharmacological activity is the primary down-
side of the PCA approach [34]. PLS analysis method resolves the issues of MLR and PCA.
The biological activity levels that are the dependent variable in PLS are also abstracted
into novel variables to enhance the correlations [35]. Three often employed techniques
for creating linear QSAR models are MLR, PCA, and PLS. The link between molecular
descriptors and biological activity is frequently nonlinear in biological systems,
though. The most popular method for dealing with nonlinear regression is a neural
network (NN). Validating the newly constructed model is a crucial component of
QSAR. In QSAR method, the cluster of molecules utilized to construct the model is
called the “training set,” whereas the set of molecules utilized for model prediction is
called the “test set.” QSAR models are validated by using two types of techniques: in-
ternal validation and external validation. Among internal validation techniques the
leave-one-out method is the commonly used, where all molecules except one which is
kept in the test set are utilized for the estimation of the coefficients of various descrip-
tors in the QS AR model using the training set, and the model created from training
set is then applied to test set molecule in order to guess it s activity. Until every mole-
cule from the training set has acted as a molecule in the test set, technique is repeated
many times. External validation, on the other hand, is making prediction about QSAR
model utilizing a t est set which was not employed to construct the model [36]. The
important QSAR methods employed are briefed in Table 12.4.
Table 12.4: Frequently utilized QSAR methods and their descriptions [11].
Method Descriptor
type
Description
HQSAR D Hologram QSAR employs molecular substructures represented as binary
patterns and fingerprints to generate molecular holograms. These binary
patterns and fingerprints are combined, and the resulting holograms are
associated with biological activity.
CoMFA D The biological activities of molecules are connected to their steric and
electrostatic characteristics through comparative molecular field studies.
CoMSIA D Comparative molecular similarity indexes contain hydrophobic terms,
and H-bond donor/acceptor in addition to steric and electrostatic
contribution.
COMBINE D Comparative binding energy analysis evaluates the ligand binding
affinity.
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12.2.3.1 Classical or 2D QSAR
In this techni que, several hydrophobic, electronic, and steric properties for conge-
neric series of compounds are associated with the biological activity, sometimes re-
ferred to as the Hansch-Fujita methodology [37, 38]. Equation (12.1) provides a typical
illustration of a relationship between physical activity and physical properties:
Log 1=cðÞ= k
1
π − k
2
π
2
+ k
3
σ + k
4
E
s
+ k
5
(12:1)
where π is called the hydrophobic substituent constant (i.e., partition coefficient), C
represents concentration of the compound necessary to elicit the activity, E
s
repre-
sents the steric substituent constant also called Taft steric parameter, and σ repre-
sents the Hammett electronic substituen t constant. 2D QSAR or Hansch analysis
typically utilizes 2D descriptors. Free-Wilson, in 1964, individually built a mathemati-
cal model that links biological activity to the presence of several chemical substitu-
ents. This model assigns an activity contribution to each type of chemical group based
on its position and influence on biological activity such as ortho, para, or meta. The
activity of the compound having substitution is then calculated as the sum of parent
molecules activity (μ) and the contributions from each of its substituents, as follows:
log
1
C
=
X
ij
a
ij
+ μ (12:2)
where a
ij
is the activity contribution at position j due to substitution i. For many years,
the Hansch and Free-Wilson techniques were used as prognostic tools in traditional
QSAR investigations [39]. Well ahead, a joint Hansch/Free-Wilson technique was created
[40] by linearly combining eqs. (12.1) and (12.2) to explain biological activity. The benefit
of the conventional QSAR is that it may describe and forecast the biological activity of a
number of related compounds using relatively basic mathematical relationships involv-
ing different physicochemical characteristics and chemical substituents.
12.2.3.2 3D-QSAR
The 3D-QSAR approach, as its name indicates, uses descriptors to characterize a mole-
cule’s 3D properties so as to create a QSAR model. The 3D properties of the ligands
may be depicted by a variety of geometrical, physical, and quantum chemical descrip-
tors using the 3D-QSAR technique. After combining these molecular descriptors, a
pharmacophore is produced which explains the biological action of the ligands. The
stability of the generated pharmacophore model and its statistical significance are as-
sessed in order to create the final 3D-QSAR model [14, 41]. Table 12.3 provides informa-
tion on the primary 3D-QSAR techniques employed for drug design.
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12.2.3.3 Multidimensional QSAR
Multidimensional QSAR involves the use of 4D and 5D descriptors to examine the en-
tire process of ligand binding, which includes factors such as solvent removal, pocket
adaptation, binding, and conformational entropy loss [42].
12.2.3.3.1 4D QSAR
The concept behind 4D-QSAR involves considering a molecule as a set of various ster-
eoisomers, tautomers, protonation states, conformations, and orientations, which is
an advancement of 3D-QSAR. By examining the spatial features of each molecule’s en-
semble sample, 4D-QSAR tries to establish a correlation between these features and
their properties. In recent times, a receptor-independent (RI) approach to 4D-QSAR
has been suggested [43]. This method involves categorizing each atom in a molecule
based on its properties such as hydrogen bond donor, polar, and nonpolar. The par-
ticles are then treated as a grid, and their interactions are considered. To generate a
Boltzmann-weighted set of conformations of the molecule inside the grid, molecular
dynamic simulations (MDSs) are conducted. To determine the likelihood of occupancy
within each alignment, various test orientations are made in the grid using different
particles and descriptors, known as grid cell occupancy descriptors. Instead of utiliz-
ing a sole conformation, this approach employs an ensemble of conformations for
each constituent in constructing the grid cell occupancy descriptors.
12.2.3.3.2 5D-QSAR
The Vedani approach was used to build 5D-QSAR, which takes into consideration re-
gional variations in binding that augment the induced fit modal for ligand binding
[44]. This technique involves creating a “mean envelope” for multiple ligands in a
training set and then comparing it with the “inner envelope” of each unique molecule
to simulate the induced fit. Various methods, such as linear scaling for topological ad-
aptation, adaptations based on lipophilicity, and property field adaptations, are uti-
lized to evaluate the induced fit models. The energy expense of the ligands adapting
to the binding site’s geometry is calculated using this information.
12.3 Structure-based computational drug design
It requires an enormous amount of time (7–12 years), money (around US $1.2 billion),
and resources in getting a new drug into a market. Also from 40,000 molecules stud-
ied on animals only five make it to clinical studies, then from these five only one is
approved. It requires purchase, production, and in vitro and in vivo studying of mil-
lions of molecules to recognize hits which are then optimized to create leads [45]. Ear-
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lier approaches such as high-throughput screening (HTS) are used to recognize a lead
molecule for a biological target, which involve testing of thousands to millions of the
sample compounds for a particular biological activity. Often the hit compound identi-
fied has stability problems, toxicity, or other subpar pharmacodynamics and pharma-
cokinetic characteristics, so this method frequently fails. Other methods such as CADD
are used, which broadly comprises structure-based drug design (SBDD) and ligand-
based drug design (LBDD). CADD uses computing resources, 3D imaging tools to de-
sign, optimize, predictive algorithms, and produce small molecules for diseases. With-
out affecting the success rate, timely discovery of undesirable compounds lowers the
overall expenses and volume of work in HTS.
SBDD is a method of CADD based on the knowledge of the configuration of a tar-
get molecule for the design of the novel drugs. The foundation of SBDD is grounded
on the accessibility of the therapeutically important target protein structures in 3D
and investigation of their binding area. This is becoming an effective and vital method
of drug discovery and academic research projects [46]. Since SBDD deals with the 3D
configuration of target protein and understanding of the diseases at the cellular stage,
it is a highly focused, effective, and rapid procedure for lead identification and opti-
mization [47]. Molecular dynamics (MD) simulations, SBVS, and molecular docking
are a few of the often employed techniques in SBDD. These techniques are employed
for wide variety of uses including the evaluation of protein-ligand interactions, bind-
ing energy values, receptor conformational changes in response to ligand binding
[48]. Many drugs have been discovered or identified using SBDD or some of its techni-
ques are enlisted in Table 12.5.
Table 12.5: List of drugs discovered by SBDD and its techniques.
Drug Target enzyme Target disease Technique References
Norfloxacin Topoisomerase II,
IV
Urinary tract
infection
SBVS []
Isoniazid InhA TB Pharmacophore modeling and SBVS []
Flurbiprofen COX- Osteoarthritis Molecular docking []
Amprenavir Antiretroviral
protease
HIV Molecular dynamics and protein
modeling
[, ]
Dorzolamide Carbonic
anhydrase
Glaucoma Fragment-based screening []
Raltitrexed Thymidylate
synthase
HIV SBDD []
Epalrestat Aldose reductase Diabetic
neuropathy
SBVS and molecular dynamics []
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The design of the target protein structure, recognition of the ligand binding area, setup
of the compound library, MDS, molecular docking and scoring functions (SF), and calcu-
lation of binding free energy are the fundamental procedures in SBDD.
12.3.1 Design of the target structure
Modern structure evaluation methods like X-ray crystallography, NMR and others are
employed for experimental determination of the 3D structure of all therapeutically
significant proteins. Without a target protein structure, in silico techniques are em-
ployed to simulate the protein’s 3D structure. Three popular techniques for predicting
structur e include comparative modeling, threading, and ab initio modeling. One of
the most effective and trustworthy methods among them is homology modeling,
which simulates the 3D configuration of the target protein using information of the
structures of homologous proteins with >40% similarity [56]. The structures generated
by these techniques are submitted and accessible in PDB. Several target protein struc-
tures are not yet determined due to the limits of experimental approaches [57].
12.3.2 Identification of the ligand binding site
Ligands bind to targets in a confined cavity to have the desired impact. Hence, it be-
comes essential to locate the binding cavity of the target protein. It is essential to have
an understanding of the ligand-binding cavity for appropriate docking. Site-directed
mutagenesis studies or X-ray crystallographic developed structures of target mole-
cules co-crystallized on substrates give knowledge of the binding sites [58]. Despite
the dynamic behavior of the protein, there are few techniques that help in identifica-
tion of the probable binding sites. For binding site mapping, these techniques help in
understanding the van der Waals (vdW) interactions and interaction energy. Regard-
ing SBDD-specific binding cavity mapping by interaction energy calculations, numer-
ous techniques have been devised. This technique pinpoints specific protein sites of
target molecule which interact well with key functional groups of potential drug com-
pounds [59]. Although there is a lack of experimental data regarding the binding site
of many proteins, there is a lot of software and web servers accessible including Meta-
Pocket [60], DoGSite Scorer [61], NSiteMatch [62], DEPTH [63], MSPocket [64], Q-SiteFinder
[59], and CASTp [4]. This enables to anticipate the potential binding locations on a target
protein. During the lead identification process, the bulky molecules that do not fit com-
fortably inside the binding cavity are excluded.
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12.3.3 Molecular docking and scoring functions
Absorption, distribution, metabolism, and toxicity factors, the Lipinski’s Rule of Five
as well as further factors like inhibition of CYP3A4 oxida tion of midazolam, hepato-
toxicity, SGOT elevation, carcinogenicity, and acute rat toxicity are used to filter drug-
like compounds prior to molecular docking [57]. The molecular level interaction be-
tween a target protein and ligand is studied using a computational technique called
molecular docking, which also enables rating of the ligands by determining how effec-
tively they bind to the receptor using various SF [65]. The commonly employed
method in SBDD is molecular docking because it precisely predicts the configuration
and interaction of ligands onto a binding site[66].Theappropriateligand-binding
configuration is ascertained by two aspects: (i) a broad configurational area identify-
ing potential binding poses; (ii) a precise prediction of the binding energy correspond-
ing to each configuration [45]. Commonly used molecular docking programs include
AutoDock [67], DOCK6 [68], CDOCKER [69], SwissDock [70], AutoDock Vina [71], GOLD
[72], FlexX [73], Surflex [74], and GLIDE [75].
A docking program can locate the ligand-binding location with the aid of a SF.
The SF determines binding affinity after a substantial binding configuration has been
determined. Therefore, it is believed that SFs have a profound effect on docking.
SFs are tested using a large volume of dataset of molecules from a related group
whose investigational data on their binding affinities exist. The four general catego-
ries of SF are ML, knowledge-based, empirical, and force field [76, 77]. The intermolec-
ular forces between the binding partners, such as vdW forces and electrostatic forces,
are estimated in order to create the force field. Based on the atom counts of the ligand
and target molecule, empirical SF are computed and utilized for affinity and also
for pose estimation [78], which involves entropy, hydrogen bonds, hydrophobic, and
hydrophilic forces. The statistical potentials of intermolecular interactions are a foun-
dational component of a knowledge-based SF. This approach is solely predicated on
the idea that a certain atom type or commonly recurring functional groups are advan-
tageous by means of energy and increase binding affinity [78]. ML approaches, unlike
standard SF, do not limit study to a specified functional form from structural charac-
teristics and binding capacity values [79]. ML approaches are active methods for
building and optimizing models to calculate binding posture and affinity. The use of
ML to generate unique SF is a common practice nowadays [80]. These methods implic-
itly help in ligand-target interactions, also overlooking interactions which are error-
prone. Also, some ML techniques including NN, support vector machine, and random
forest work with nonlinear reliance from binding interfaces. Consequently, the SF
centered on ML overtake others in binding energy estimations [81].
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12.3.4 Virtual screening
Virtual screening (VS) is a reliable way for identifying leads [82]. In VS, databanks con-
taining a large number of lead-like or drug-like molecules undergo computational in-
vestigation against target proteins having already-established 3D structures. Docking
is used to screen chemical libraries, and ligands are screened based on their ability to
bind [83, 84]. The top computationally screened hits are subsequently examined in vi-
tro [82, 85]. VS is of two types: SBVS and LBVS (ligand-based VS). Biological data is ana-
lyzed during LBVS to differentiate active compounds from inactive ones. The data
generated is then used to recognize adequately active scaffolds on the consensus
pharmacophore similarity or other characteristics [86]. Understanding the target pro-
tein’s 3D structure is essential for SBVS. Computer algorithms link the target protein
with the vast molecular libraries of pharmaceutically similar substances that are
available publicly. In order to determine the docked complex’s binding strength, a SF
is used. This is continued by investigational tests to confirm the interaction.
12.3.5 De novo drug design
It is a method of producing novel drug-like molecules beginning with molecular building
blocks. The computer simulation aids in the process for de novo drug design. The com-
puter simulation, for instance, aids in identifying potential binding areas on the target
protein during fragment-based drug development, which can subsequently be utilized to
dock tiny drug-like molecules. Additionally, computer simulation can be utilized in SBDD
to simulate how tiny molecules attach to their targets [87], assisting in predicting how
the molecule will interact with the protein and directing the design process. Overall, the
computer simulation enables more efficient and successful drug discovery and offers a
strong support regarding de novo drug design. Two types of designs, positive and nega-
tive, are used. In the former design, a study is restricted to specified chemical space
areas with a better likelihood of ascertaining hits with the required properties. In com-
parison, the negative mode’s study criteria are predetermined to evade choosing false
positives [88]. There are two approaches that can be used: (i) receptor-based and (ii) li-
gand-based de novo drug design. The former strategy is commonly employed; as appro-
priate small compounds are created by fixing specific parts into the binding sites on the
receptors, target protein quality and precise understanding of their binding cavity are
crucial regarding receptor-based drug design. This could be accomplished using a com-
puter program or by cocrystallizing the ligand and receptor [89]. For receptor-based de-
sign, there are two methods: either building blocks such as atoms or groups like
hydrocarbons, amines, and single ring systems are linked to each other to produce an
entire chemical complex or just by developing a ligand using only one component. The
binding site is found in the fragment-linking approach to map probable binding areas
for numerous functional groups present in a specific part of a molecule [90]. To create
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an absolute compound, these functional groups are joined. The binding site is used to
grow fragments in the fragment-growing process, which is then monitored by the right
search algorithms [91]. These search algorithms engage scoring algorithms to regulate
the chance of progression. The entire chemical space is utilized in fragment-based de
novo drug design to design new molecules, and also the choice of linkers is crucial when
using the linking strategy [48].
12.3.6 Molecular dynamics
MDSs offer insights into the interactions between proteins and ligands, offering crucial
insights into understanding the structure–function relationship of a drug target. This
knowledge serves as a valuable guide in the drug design process. Once a simulation is
established, researchers gain a comprehensive understanding of how a drug can dis-
rupt the biological target’s pathophysiology [92]. Various force fields are frequently em-
ployed in MDSs such as AMBER, CHARMM, and GROMOS. While these differ primarily
in their parameterization methods, they typically yield comparable results. Pioneering
research conducted by Karplus and McCammon as well as Warshel and Levitt eluci-
dated the pivotal role of classical MDSs in the exploration of biological systems. Their
work involved the utilization of MD simulations to generate diverse protein and nucleic
acid conformations including early endeavors to simulate intricate phenomena like pro-
tein folding. Over the past few decades, the scientific community has increasingly come
to appreciate that MD can address the significant drawbacks inherent in static SBDD.
These limitations are particularly evident in common ligand docking calculations,
which often fail to capture the signif icant protein conformational changes that fre-
quently occur during ligand binding. The fundamental concept behind MD simulations
revolves around the examination of the dynamic evolution of microscopic systems over
time. This entails the solution of second-order differential equations, which are a repre-
sentation of Newton’s second law:
f
i
ðtÞ = m
i
a
i
ðtÞ = −
∂VðxðtÞÞ
∂x
i
ðtÞ
where f
i
(t) is the net force acting on the ith atom of the system at a given point in
time t, a
i
(t) is the corresponding acceleration, and m
i
is the mass.
In the context of drug discovery, the conventional model systems consist primar-
ily of the solvated target and ligand(s), typically involving a size of a few hundred
thousand atoms or less. Over recent years, unbiased MD simulations of these models
have found widespread use in elucidating the mechanisms underlying drug binding
to various target systems. Several series of ~100 ns simulations were conducted with-
out bias to investigate potential pathways for the spontaneous binding of benzami-
dine to trypsin [93].
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12.4 Conclusions
In conclusion, the computational studies have emerged as indispensable tools in the
field of enzyme inhibitor discovery. They enable efficient screening and rational de-
sign, significantly expediting the identification of potential drug candidates. The inte-
gration of LBDD and SBDD app roaches enhances the efficiency and success rate of
enzyme inhibitor discovery. These hybrid strategies allow for a more comprehensive
exploration of chemical space. Overall, the computational studies have revolutionized
the field of enzyme inhibitor discovery, enabling researchers to make informed deci-
sions, optimize lead compounds, and accelerate the development of novel therapeutic
agents. Their continued advancement promises even greater contributions to the fu-
ture of drug discovery and healthcare.
Abbreviations
3D three-dimensional
CADD computer-aided drug design
HTS high-throughput screening
LBDD ligand-based drug design
LBVS ligand-based virtual screening
MD molecular dynamics
ML machine learning
NMR nuclear magnetic resonance
NN neural network
PDB protein data bank
SBDD structure-based drug design
SBVS structure-based virtual screening
SF scoring functions
SVM support vector machine
VdW van der Waals
VS virtual screening
References
[1] Yu, W., & MacKerell, A. D. Computer-aided drug design methods. Antibiotics: Methods and
Protocols, 2017, 1520, 85–106.
[2] Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. Role of computer-aided drug design in modern drug
discovery. Archives of Pharmacal Research, 2015, 38, 1686–1701.
[3] Duch, W., Swaminathan, K., & Meller, J. Artificial intelligence approaches for rational drug design
and discovery. Current Pharmaceutical Design, 2007, 13(14), 1497–1508.
[4] Gurung, A. B., Ali, M. A., Lee, J., Farah, M. A., & Al-Anazi, K. M. An updated review of computer-aided
drug design and its application to COVID-19. BioMed Research International, 2021, 2021, 1–16.
312 Mohammad Ovais Dar et al.
https://t.me/med1917
[5] Hassan Baig, M., Ahmad, K., Roy, S., Mohammad Ashraf, J., Adil, M., Haris Siddiqui, M., Khan, S.,
Amjad Kamal, M., Provazník, I., & Choi, I. Computer aided drug design: Success and limitations.
Current Pharmaceutical Design, 2016, 22(5), 572–581.
[6] Jorgensen, W. L. The many roles of computation in drug discovery. Science, 2004, 303(5665),
1813–1818.
[7] Prathipati, P., Dixit, A., & Saxena, A. K. Computer-aided drug design: Integration of structure-based
and ligand-based approaches in drug design. Current Computer-Aided Drug Design, 2007, 3(2),
133–148.
[8] Fons, N. Focus: Drug development: Textbook of drug design and discovery. The Yale Journal of
Biology and Medicine, 2017, 90(1), 160.
[9] Johnson, M. A., & Maggiora, G. M. Concepts and Applications of Molecular Similarity. 1990, Wiley.
[10] Mestres, J., Martín-Couce, L., Gregori-Puigjané, E., Cases, M., & Boyer, S. Ligand-based approach to
in silico pharmacology: Nuclear receptor profiling. Journal of Chemical Information and Modeling,
2006, 46(6), 2725–2736.
[11] Sharma, V., Wakode, S., & Kumar, H. Structure-and ligand-based drug design: Concepts,
approaches, and challenges. Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences,
2021, 3, 27–53.
[12] Bajorath, J. Selected concepts and investigations in compound classification, molecular descriptor
analysis, and virtual screening. Journal of Chemical Information and Computer Sciences, 2001, 41(2),
233–245.
[13] Leach, A. R., & Gillet, V. J. An Introduction to Chemoinformatics. 2007, Springer.
[14] Acharya, C., Coop, A., E Polli, J., & D Mackerell, A. Recent advances in ligand-based drug design:
Relevance and utility of the conformationally sampled pharmacophore approach. Current
Computer-aided Drug Design, 2011, 7(1), 10–22.
[15] Marrero-Ponce, Y., Santiago, O. M., López, Y. M., Barigye, S. J., & Torrens, F. Derivatives in discrete
mathematics: A novel graph-theoretical invariant for generating new 2/3D molecular descriptors.
I. Theory and QSPR application. Journal of Computer-aided Molecular Design, 2012, 26(11),
1229–1246.
[16] Bajorath, J. Integration of virtual and high-throughput screening. Nature Reviews Drug Discovery,
2002, 1(11), 882–894.
[17] Auer, J., & Bajorath, J. Molecular similarity concepts and search calculations. Bioinformatics:
Structure, Function and Applications, 2008, 453, 327–347.
[18] Willett, P. Similarity-based virtual screening using 2D fingerprints. Drug Discovery Today, 2006,
11(23–24), 1046–1053.
[19] Bero, S. A., Muda, A. K., Choo, Y. H., Muda, N. A., & Pratama, S. F. Weighted Tanimoto coefficient for
3D molecule structure similarity measurement. 2018.
[20] Lipinski, C., Lombardo, F., Dominy, B., & Feeney, P. CAS: 528: DC% 2BD3MXitVOhs7o% 3D:
Experimental and computational approaches to estimate solubility and permeability in drug
discovery and development settings. Advanced Drug Delivery Reviews, 2001, 46(1–3), 3– 26.
[21] Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. Molecular
properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry,
2002, 45(12), 2615–2623.
[22] Teague, S. J., Davis, A. M., Leeson, P. D., & Oprea, T. The design of leadlike combinatorial libraries.
Angewandte Chemie International Edition, 1999, 38(24), 3743–3748.
[23] Egan, W. J., Merz, K. M., & Baldwin, J. J. Prediction of drug absorption using multivariate statistics.
Journal of Medicinal Chemistry, 2000, 43(21), 3867–3877.
[24] Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., & Shoichet, B. K. Relating
protein pharmacology by ligand chemistry. Nature Biotechnology, 2007, 25(2), 197–206.
12 Computational approaches for enzyme inhibition discovery 313
https://t.me/med1917
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