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153
7. 1 Introduction
Target recognition and lead compound discovery are currently leading developments in drug
discovery, with an emphasis on the causes of diseases and their comprehension. A personalized
medicine system dependent on the conversion of molecular phases (and developments, from DNA
to RNA to protein) has become essential in the development of drugs in the era of personal medi-
cine and more updated economic healthcare outcomes [1, 2]. The molecular identification of ill-
ness is an essential requirement for developing a system like this, and environmental factors and
the gut microbiota must also be taken into account. Safety regulations are becoming more strin-
gent at the same time [3, 4]. We believe that to deal with the previously discussed interaction in a
high-throughput form, data technology and chemoinformatic methods should be used in concert
with human intelligence and artificial interplay. While people are limited in their ability to identify
patterns by data-intensive and intellectually challenging environments and procedures computer
algorithms may not be able to do so [5]. This coordination will support group data techniques and
help in direct judgment calls, and proper and quick effective data output. Large and varied data
sets necessitate stringent screening as well as in-depth analysis and interpretation. Scientists work-
ing in biomedicine must simultaneously collaborate while making selections in an efficient and
effective manner. Large-scale volumes of intricate, multifaceted data must be meaningfully gath-
ered, mined, and analyzed to achieve this in such an environment; computer-aided drug discovery
will be more efficient when methodologies for selecting targets and verification are dependable.
Additionally, new computational frameworks grounded in systems biology and networks incorpo-
rate omics databases and improve combinational drug development strategies. In such an environ-
ment, more effective computer-aided drug discovery will be made possible by reliable target choice
and verification combined with drug discovery methodologies. Furthermore, new computational
models based on networks and systems biology integrate omics databases and enhance combina-
tional drug development approaches [6].
7
In Silico Modeling and Drug Design
Sonali S. Shinde
1
, Sanket S. Rathod
2
, and Sohan S. Chitlange
1
1
Department of Pharmaceutical Chemistry, Dr. D. Y. Patil Institute of Pharmaceutical Sciences and Research, Pune, Maharashtra, India
2
Department of Pharmaceutical Chemistry, Bharati Vidyapeeth College of Pharmacy, Kolhapur, Maharashtra, India
7 In Silico Modeling and Drug Design154
7. 2 Target Identification
A biological entity, typically a protein, that can modify the symptoms of a disease is referred to as
a target for drugs [7]. Therefore, the first and most crucial phase in the drug discovery system is the
recognition of prime drug targets. Traditional methods of discovering drug targets involve conducting
experiments to find genes that are expressed differently in healthy and infected cells or tissues, as
well as proteins that have strong connections to proteins associated with the disease.
7.2.1 Experimental Approaches
Target recognition using traditional experimental techniques necessitates biochemical and
molecular investigations of disease pathophysiology. Although these investigations depend on
the information on different disorders, there may be long-duration methods of locating possible
treatment targets. Genome-scale testing methods like target deconvolution, haploinsufficiency
profiling (HIP), and solid isotope identifying by amino acids in cell culture (SILAC) have been
introduced recently to improve target recognition. HIP is an assay where genome-wide investi-
gation is done to determine possible drug targets, finds gene products linked to the sensitivity of
disease cell lines, and exposes cells to chemical compounds [8]. One benefit of the HIP assay is
that it can assess thousands of genes simultaneously and does not require any prior understand-
ing of the pathophysiology of the disease. With traditional drug discovery methods, it is difficult
to recognize viable therapeutic targets because of the complex pathophysiologies of several ill-
nesses. In such an example, a different way might be employed: drugs that modify the symptoms
of a disease could be found through screening, which would reveal target proteins matching
criteria [9]. A variety of techniques, including affinity chromatography, biochemical suppres-
sion, and protein microarrays, are used for target deconvolution [10]. Figure 7.1 gives an idea of
strategies for target identification. The effective reverse screening method known as SILAC
Target identification
Have a
compound
Target
deconvolution
Target
discovery
One
target
Target validation
Want a
compound
Figure 7. 1 An overview of target identification strategies.
7. 2 arget Identification 155
makes it possible to identify target proteins that are bound to medications and small-molecule
probes in an objective, thorough, and reliable manner [11]. More precise determination of drug–
protein interactions is now possible thanks to the recent integration of this method with affinity
chromatography and quantitative mass spectrometry-based proteomics. Although SILAC has
many advantages, its wide-ranging and practical use is limited by a number of drawbacks such
as: (i) labeling of an isotope is expensive; (ii) complex equipment, such as (HRMS) high-
resolution mass spectrometers, is needed; and (iii) it takes time to generate chemically immobi-
lized drugs and ensure their biological activity [12].
7.2.2 Computational Target Identification
Experimental techniques are costly and usually applied at lower throughput scales because of
their complex nature. To overcome these hurdles, computational techniques have been created
to determine the best pharmacological targets [13]. The targeted proteins can be calculated
computationally using actual experiment results [14, 15], anticipated from protein networks, or
extracted from the literature survey [16]. Several web servers provide the names of possible
drug targets that have been calculated using various databases, like Harmonizome and the
Open Target Platform [17]. Instead, the concept that similar-structured ligands could attached
to the same protein with the same binding energies and exhibit the same biological properties
can be applied to find putative protein targets through the use of a reverse docking tech-
nique [18, 19]. One popular method for identifying drug targets is association-based recogni-
tion. As an illustration, the Open Targets Platform [20] incorporates information from a variety
of sources, such as text-mined information from the scientific literature, animal model out-
comes of experiments, and omics data. After that, the platform ranks genes based on how
closely they are linked to illness [21]. Several machine learning and statistical methods such as
the Similarity Ensemble method [22], PharmMapper, Tarfisdock, and TargetHunter have been
established to determine the biological target of a drug in a pharmaceutical formulation [23–25].
A common method of protein target discovery is ligand-based when there is nonavailability of
drug pathophysiology information. Lavecchia investigated several machine-learning methods
created to process ligand finding by utilizing fingerprints and molecular descriptors that cor-
respond to the physical–chemical characteristics of a drug [26]. Often applied in the making of
descriptors, fingerprints, and predictive models offer a representation of the physical and
chemical characteristics of a substance [27]. A subtractive approach might help achieve planned
objectives more accurately. For example, the best therapeutic targets for the treatment of
Helicobacter pylori infection can identify removing extracellular enzymes from respective
humans and or gut flora, nonessential proteins, redundant enzyme, and materials from the
H. pylori proteome [28] (Table 7.1).
7.2.3 Target Validation
Verifying if altering a biological function of a target influences the disease phenotype comes next
after a target has been determined [37]. Modulating biological processes and assessing estimated
targets can be done in a number of ways. Small interfering RNAs (siRNAs) [38] are the most popu-
lar of these techniques because they may replicate the effects of drugs by repressing translation,
which causes the target protein to be temporarily suppressed [39, 40]. With siRNAs, target inhibi-
tion can be studied without the need for inhibitors or prior protein structure information [41].
However, the level of suppression by siRNAs can impact cellular physiologies variously and may
7 In Silico Modeling and Drug Design156
therefore result in incompatible effects for conditions with complicated pathophysiology such as
neurological disorders [42]. In these situations, animal models with mutated or deleted target
genes may provide more useful information for target approval.
7. 3 Computer-Aided Drug Design
Drug discovery aims to find small substances so those can change the roles of an identified target
protein; thus consequently, there is a change in the disease phenotype. Additionally, it is imperative
to find small molecules with good pharmacokinetic parameters and minimum cell toxicity.
A lengthy, expensive, and tedious series of complex steps, including pharmacokinetics, preclinical
toxicity assessments, applicant confirmation, and drug candidate identification, are involved in the
search for new pharmaceuticals. Traditional pharmaceutical research and development (R&D) is
expensive and has a long duration. A drug specifically requires 10–14 years to come into the market,
and each successful drug discovery is thought to have cost between $900 million and $1.9 billion
USD [43]. Finding chemical compounds that are pharmacologically operational is the first chal-
lenge in the drug discovery process. In general, experimental HTS has a hit rate of 0.01–0.14% [44].
Approximately 40–70% of drug failures afterward are caused by deficiencies in ADME-Tox, which
Table 7.1 Protein target databases.
Database Description URL Reference
Drug bank A collection of 13,857 drug entities
including 2661 approved drug molecules
and 1425 approved biologics (peptides,
proteins, and vaccines)
https://go.dru
http://gbank.com
[29]
ChEMBL A collection of 13,382 drug targets and
1.9 million drug molecules
https://www.ebi.ac.uk/
chembl
[30]
ChemBank Information on hundreds of biomedical
assays and millions of small drug
compounds
https://data.broa
http://dinstitute.org/
chembank/assay
[31]
Therapeutic Target
Database
Experimental validation data on 37,316
drug compounds and 3419 drug targets
http://db.idrblab.net/ttd [32]
Traditional
Chinese Medicines
Information on 37,170 unique
compounds from 352 different herbs,
minerals, and animal products
http://tcm.cmu.edu.tw/ [33]
MATADOR Information on manually annotated
drugs and targets from drug bank and
super targets
http://matador.embl.de/ [34]
ChemSpider Structural and text information on over
67 million chemical compounds
http://www.ch
http://emspider.com
[35]
Chem2BioRDF Information on chemical compounds,
biological targets, and phenotypic data
http://chem2bio
http://2rdf.org
[35]
Promiscuous Information on 991,805 small molecules,
9430 drug targets and 2,727,520
drug–target interactions
http://bioinfo
http://rmatics.charite.
depromiscuous2/index.php
[36]
7. 3 Computer- ided Drug Design 157
is another major obstacle [45, 46]. The use of in silico drug development technology has proven to
be very beneficial to any pharmaceutical company for a long time [47]. The two key advantages of
computational drug designing are it is cost effectiveness and time-saving capabilities. Moreover, it
can be used in every stage, from drug testing to preclinical–clinical phases [48], greatly reducing
the likelihood that drug procedures will not succeed. Thanks to recent developments in machine
learning algorithms, gathered databases, and readily procured synthetic drug substance lists, in
silico methods can practically test a wide range of chemical molecules and then immediately assess
their ADMET aspects and identify potential pharmaceuticals with good potency and less cell
toxicity [49]. Figure 7.2 shows the main two ways of computer-aided drug design techniques:
(i) ligand-based approach and (ii) structure-based approach.
7.3.1 Ligand-based CADD
Ligand-based CADD techniques search for novel, more effective compounds by utilizing information
about small molecules which interact with the target in concern. This data covers physicochemical
characteristics, chemical structure, binding affinities, etc. These approaches are thought to be
more effective than structure-based approaches in some circumstances. Selecting novel com-
pounds based on their chemical resemblance to well-established active ones is one ligand-based
strategy. Numerous fingerprint techniques are available for this purpose, enabling the effective
visualization of a molecular structure in comparison to other molecules [51]. These techniques,
which are based on chemical-based information about compounds, provide an extremely qualita-
tive method for finding novel, best-effective ligands [29]. A suitable method for the determination
of T-type blockers of calcium channels has been linked to neuropathic pain and seizures. This
work was presented by Ijjaali and peers [30]. In this work, a ligand-based online testing procedure
was carried out on a two million compound database by using Chem Axon’s PF and CCG’s
Computer-aided drug
design techniques
Ligand-based
approaches
Example: SAR, QSAR,
pharmacophore
modelling, ligand-
based virtual screening
Requires the 3D
Structure of the target
Example:
ligand docking,
de novo design,
molecular
dynamics
This indirect approach
can be implemented
when 3D structure of a
target is unavailable
Structure-based
approaches
Figure 7. 2 Classification of computer-aided drug design (CADD) techniques and mainly applied
techniques. SAR, structure–activity relationship; QSAR, quantitative structure–activity relationship.
Source: Koszła et al.[50]/MDPI/CC BY 4.0.
7 In Silico Modeling and Drug Design158
GpiDAPH3 fingerprints tools to evaluate 38 molecules for their good functionality on recombinant
human CaV3.2. Half of the 38 molecules were potent, meaning that these hits were blocking the
T-type current mediated by CaV3.2. The quantitative structure–activity relationship (QSAR) is
another crucial method, where a QSAR model can clarify the connection between a group of mol-
ecules’ structures and their intended response. Gathering a number of both inactive and active
compounds against a targeted protein and creating descriptors that characterize their fundamental
and physicochemical parameters make up the standard QSAR workflow. A forecasting tool for
novel molecular entities can then be produced by using the model to connect these descriptors
with the laboratory activity [52]. QSAR algorithms, which are always changing, can be used to
implement a variety of 2D and 3D descriptors, including volume, molecular weight, distance
between two atoms, types of atoms, rotatable bonds, electronegativity, atom distribution, solving
characteristics, and aromaticity. There are multiple levels of definition for these descriptors, each
with increasing complexity. This strategy works well for finding hit compounds that act as mGlu5
allosteric modulators, as evidenced by a recent study. A well-known pharmacological target for
schizophrenia, Parkinson’s disease, and anxiety is mGlu5 [53]. Mueller and colleagues generated a
QSAR model using data from an earlier mGlu5 HTS screen [54], which allowed them to find 28
active pharmaceutical compounds that alter the signaling pathways of proteins. While HTS had a
0.2% hit rate, the QSAR model had an overall success rate of 3.6% [55, 56]. Proteochemometrics
(PCM) and polypharmacology modeling is another area receiving attention in the context of com-
putational drug discovery [57]. Using a single predictive model, PCM modeling integrates target
and ligand data to forecast an outcome diverse of interest [58, 59]. Personalized medicine is made
achievable by integrating information from targeted protein and ligand information into a unique
machine-developing model, as opposed to relying solely on ligand- or protein-only approaches to
determine the optimal drug therapy for a given genotype [60]. Applying PCM techniques, Frimurer
et al. were able to find roughly 60 compounds for the prostaglandin D-2 receptor 2 (CRTH2) after
testing of a list of 1.4 million structures [61, 62].
7.3.2 Structure-Based CADD
The use of 3D protein and DNA data in drug design started almost 30 years ago. The Protein
Databank (PDB) is the biggest source of biomolecule structure data, primarily from X-ray crystal-
lography and NMR methods. 1998 saw the addition of 2058 structures in all to the protein data
bank. Till now, almost there have been 105,465 structures total as of 2014, an increase in deposi-
tions of roughly 7.5% per year. For the past few years, the pharmaceutical industry and academia
have been using this abundance of structural data to inform their drug design strategies. Dynamic
macromolecules by nature are proteins. An interaction of protein with a small molecule cannot be
fully understood from a structured snapshot, nor can its binding site be determined. Molecular
dynamics (MD) simulations represent one of the most significant facets of protein behavior
research [34, 63]. In MD simulations, dihedral angles are modeled applying a sinusoidal function,
while chemical interactions and atomic angles are modeled applying straightforward online
springs. Since the hydrophobic and electrostatic interactions are computed employing Lennard-
Jones potential and Coulomb’s law, nonbonded forces like van der Waals interactions. These simu-
lations have a big influence on the drug discovery industry when combined with experimental
data. Historically, these computations have been carried out on CPU clusters using software that
can parallelize complex system simulation processes [64]. The computations needed for these sim-
ulations have been established recently so that computer graphics and video game applications can
handle them. MD simulations have eventually been accelerated by the graphics processing units
7. 3 Computer- ided Drug Design 159
(GPUs), which were initially created to speed up video games, typically by magnitude range [33,
65]. Many MD functions are applied for the free energy calculation to compare the estimated
values with the determined by experiment binding capacities of small drugs to a targeted protein.
Molecular mechanisms Poisson–Boltzmann surface area (MM/PBSA), and linear interaction
energy (LIE), free energy perturbation (FEP) techniques are some of these software programs [36,
66]. They can then be used for in silico affinity binding prediction after that. Among the applica-
tions of these techniques are the prediction of binding free energies, the forecasting of the binding
mode of efavirenz to HIV-1 RT by MM-PBSA, the application of LIE models to predict the binding
mode of β-secretase (BACE) inhibitors, and calculations of relative binding free energy of biotin
and the interactions of analogs with streptavidin using FEP strategies [67]. Sometimes the struc-
ture of targeted protein for a drug design project remains a mystery. Here are some predicted tools
for developing comparative models. Comparative modeling is utilized to determine the structure
of a protein from a structural template because protein molecules with the same sequences typi-
cally have the same structures. The main applied in silico technique to accomplish this is the
homology modeling, which creates a protein model by identifying a structurally related template
protein with the same series, aligning their sequences, applying aligned region coordinates, pre-
dicting the construction of the model, and fine-tuning the target’s missing atom coordinates.
MODELER is one of the most popular programs for homology modeling [68] and SWISS-
MODEL [69]. A chemo-attractant model of the receptor OXE-R, which is founded on the CXCR4
crystal structure [35] as a template, is one instance of how homology modeling is used in drug
design. Consequently, a small-molecular modulator called Gue1654 was found via virtual screen-
ing techniques to inhibit a particular GPCR signaling pathway [70]. An additional instance is
the prediction of the binding energy mechanism of antihypertensive medications to the type 1
angiotensin-II receptor (AT1). The receptor with a homology model and an investigation of the
pharmaceutical binding mode utilizing pharmacophore and MD modeling were later confirmed
upon the discovery of the crystal structure. When molecules are bound together to create a stable
complex, docking [71] techniques are employed to predict which orientation a molecule will prefer
to occupy on a protein. The versatility of the compounds or the targeted protein via the docking
process is taken into account in different ways depending on the method [72, 73]. The most widely
used approach holds that the protein docking site is rigid and that the ligand is flexible, typically
after applying force fields from MD. Several software programs, including SURFLEX, Autodock,
Gold [74], DOCK, AutoDock Vina, GLIDE [75], and others, are available for docking. Scoring func-
tions are used to calculate the docking score, which is an assessment of the energetic affinity of the
complex. These scoring functions may be empirical, knowledge-based, consensus-based, or based
on molecular mechanics. For instance, SURFLEX employs an empirical function to assess the
binding energetics, whereas DOCK applies the AMBER force field. Consensus scoring is a widely
researched technique that predicts the binding energies of a compound for a specific target by
combining multiple scoring algorithms. In one case study [76], Tuchinardi et al. determined the
overall docking and scoring of multiple algorithms along 83 ligand–receptor X-ray structures. One
more familiar technique for determining how likely it is for a molecule to attach to a targeted pro-
tein binding location is the pharmacophore modeling method. A pharmacophore is the set of elec-
tronic and steric properties needed to confirm the best supramolecular interaction with a typical
structure of a biological target [77]. The complex of ligand and target protein can be mapped using
pharmacophore features, which show the geometrical and chemical properties. These parameters
cover basic and acidic groups, hydrogen bond donors and acceptors, and aliphatic and aromatic
hydrophobic moieties, partial charge [78]. These features involve hydrogen bond donors and
acceptors, acidic and basic groups, partial charge, and aromatic and aliphatic hydrophobic
7 In Silico Modeling and Drug Design160
molecules [79]. This depiction can be utilized in online testing assignments to find effective bind-
ing sites on the basis of the interaction. Pharmacophore modeling has been incorporated into
many software applications. For example, The Pocket v.2 and Ligandscout map ligand–target inter-
actions using algorithms found in complex protein–ligand data targets.
7. 4 ADMET Assessment
The next step after finding a drug candidate is to evaluate its pharmacokinetic characteristics, like
ADMET. Thanks to developments in machine learning algorithms and gathered datasets, compu-
tational methods can also be used to predict ADMET [80]. Preclinical studies are thought to with-
draw 40–60% of drug candidates due to ADMET concerns. To reach their biological targets and
produce their pharmacological effects, drug compounds must pass through several physiological
barriers, including the blood–brain barrier, the gastrointestinal barrier, and microcirculatory bar-
riers. They might need to be metabolically changed to be activated, or they might change into a
hazardous substance with unfavorable effects [81]. For ADMET evaluations, conventional experi-
mental techniques are still time-consuming and expensive. Lipinski’s called as “rule of five,” which
is a molecular weight <500 Da, lipophilicity <5, number of rotatable bonds <10, hydrogen bond
donors <5, and hydrogen bond acceptors <10, is a straightforward guideline for determining
whether a chemical compound is drug-like [82]. More sophisticated prediction techniques are
being used more frequently these days to forecast drug likeness in the form of ADME-Tox charac-
teristics, as opposed to relying solely on this basic guideline. Several models based on machine
learning have been created to forecast the pharmacokinetic characteristics of chemical substances.
ALOGPS, PreADMET, DrugMint, and SwissADME are a few of these models. For instance,
Schyman et al. used the variable nearest neighbor (vNN) approach to build 15 models for the pre-
diction of ADMET properties [83]. Additionally, Abdul et al. used a decision tree algorithm to cre-
ate a model for predicting chemical toxicity. The method was applied for toxicity screening after
they selected an ideal number of characteristics from a list of parameters. Furthermore, Yu et al.
developed a model for predicting androgen receptor toxicity using a coevolutionary neural net-
work algorithm [84]. Remarkably, they trained the model using 2D chemical structure photos of
molecules rather than the widely used molecular descriptors. Their model effectively categorized
substances that are inactive and agonists of androgen receptors. Ultimately, Lee et al. utilized a
neural network algorithm to create a trustworthy cardiotoxicity prediction model for the human
ether-a-go-go-related gene (hERG).
7. 5 Conclusion
The efficiency and accuracy of in silico drug targets and therapeutic drug identification have
increased over the last few decades. The accumulation of freely accessible biological data and the
quick development of computational techniques have recently led to an acceleration in in silico
drug discovery. The biological functions of targets are explained by chemical biology, and to help
identify promising drug candidates, CADD techniques use structural data about either ligands or
structure based or with referred to bioactivity (ligand based). Thanks to its capacity to expedite the
process of finding new drugs by utilizing pre-existing information on ligand–receptor interactions,
structural optimization, and synthesis, CADD techniques have become an indispensable compo-
nent of the drug discovery process. The key pharmacological characteristics for a successful drug
References 161
development strategy are adsorption, distribution, metabolism, excretion, and toxicity. A growing
number of models based on machine learning have also been created using biological data. As of
this writing, a number of drugs discovered through CADD techniques are available to consumers
and have reached the market. Further developments are needed, though, especially in the areas of
molecular docking scoring functions, identifying receptors in the absence of structural data, add-
ing solvent effects and molecular adaptability to force fields used in MD simulations, and increas-
ing computational efficiency. By taking care of these limitations of CADD approaches, all the
possibilities of CADD may be realized.
References
1 Barabási, A.L., Gulbahce, N., and Loscalzo, J. (2011). Network medicine: a network-based
approach to human disease. Nature Reviews Genetics [Internet] 12 (1): 56–68. https://doi.
org/10.1038/nrg2918.
2 Chen, B. and Butte, A.J. (2013). Network medicine in disease analysis and therapeutics. Clinical
Pharmacology and Therapeutics 94 (6): 627–629.
3 Culligan, E.P., Sleator, R.D., Marchesi, J.R., and Hill, C. (2014). Metagenomics and novel gene
discovery: promise and potential for novel therapeutics. Virulence 5 (3): 37–41.
4 Wang, L. and Xie, X.Q. (2014). Computational target fishing: what should chemogenomics
researchers expect for the future of in silico drug design and discovery? Future Medicinal Chemistry
6 (3): 247–249.
5 Agrawal, D., Bernstein, P., Bertino, E. et al. (2011). Challenges and Opportunities with Big Data 2011-1.
1–16. Available from: https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1000&context=cctech.
6 Tsiliki, G., Karacapilidis, N., Christodoulou, S., and Tzagarakis, M. (2014). Collaborative mining
and interpretation of large-scale data for biomedical research insights. PLoS One 9 (9): e108600.
7 Katsila, T., Spyroulias, G.A., Patrinos, G.P., and Matsoukas, M.T. (2016). Computational
approaches in target identification and drug discovery. Computational and Structural Biotechnology
Journal [Internet] 14: 177–184. https://doi.org/10.1016/j.csbj.2016.04.004.
8 Giaever, G., Shoemaker, D.D., Jones, T.W. et al. (1999). Genomic profiling of drug sensitivities via
induced haploinsufficiency. Nature Genetics 21 (3): 278–283.
9 Kubota, K., Funabashi, M., and Ogura, Y. (2019). Target deconvolution from phenotype-based drug
discovery by using chemical proteomics approaches. Biochimica et Biophysica Acta (BBA) –
Proteins and Proteomics [Internet] 1867 (1): 22–27. https://doi.org/10.1016/j.bbapap.2018.08.002.
10 Ong, S.E., Schenone, M., Margolin, A.A. et al. (2009). Identifying the proteins to which small-
molecule probes and drugs bind in cells. Proceedings of the National Academy of Sciences of the
United States of America 106 (12): 4617–4622.
11 Ong, S.E., Blagoev, B., Kratchmarova, I. et al. (2002). Stable isotope labeling by amino acids in cell
culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular
Proteomics [Internet] 1 (5): 376–386. https://doi.org/10.1074/mcp.M200025-MCP200.
12 Chan, J.N.Y., Nislow, C., and Emili, A. (2010). Recent advances and method development for drug
target identification. Trends in Pharmacological Sciences 31 (2): 82–88.
13 Jenkins, J.L., Bender, A., and Davies, J.W. (2006). In silico target fishing: Predicting biological
targets from chemical structure. Drug Discovery Today: Technologies 3 (4): 413–421.
14 Ezzat, A., Wu, M., Li, X.L., and Kwoh, C.K. (2018). Computational prediction of drug–target
interactions using chemogenomic approaches: an empirical survey. Briefings in Bioinformatics
20 (4): 1337–1357.
7 In Silico Modeling and Drug Design162
15 Cichonska, A., Ravikumar, B., Parri, E. et al. (2017). Computational-experimental approach to
drug–target interaction mapping: a case study on kinase inhibitors. PLoS Computational Biology
13 (8): 1–28.
16 Zheng, S., Dharssi, S., Wu, M. et al. (2019). Text mining for drug discovery. Methods in Molecular
Biology 1939: 231–252.
17 Agamah, F.E., Mazandu, G.K., Hassan, R. et al. (2020). Computational/in silico methods in drug
target and lead prediction. Briefings in Bioinformatics 21 (5): 1663–1675.
18 Rouillard, A.D., Gundersen, G.W., Fernandez, N.F. et al. (2016). The harmonizome: a collection of
processed datasets gathered to serve and mine knowledge about genes and proteins. Database: The
Journal of Biological Databases and Curation 2016: 1–16.
19 Ochoa, D., Hercules, A., Carmona, M. et al. (2021). Open targets platform: supporting systematic
drug-target identification and prioritisation. Nucleic Acids Research 49 (D1): D1302–D1310.
20 Byrne, R. and Schneider, G. (2019). In silico target prediction for small molecules. Methods in
Molecular Biology 1888: 273–309.
21 Chen, Y.Z. and Zhi, D.G. (2001). Ligand–protein inverse docking and its potential use in the
computer search of protein targets of a small molecule. Proteins: Structure, Function, and Genetics
43 (2): 217–226.
22 Paul, N., Kellenberger, E., Bret, G. et al. (2004). Recovering the true targets of specific ligands by
virtual screening of the protein data bank. Proteins: Structure, Function, and Genetics 54 (4):
671–680.
23 Li, H., Gao, Z., Kang, L. et al. (2006). TarFisDock: a web server for identifying drug targets with
docking approach. Nucleic Acids Research [Internet] 34 (Suppl_2): W219–W224. https://doi.
org/10.1093/nar/gkl114.
24 Carvalho-Silva, D., Pierleoni, A., Pignatelli, M. et al. (2019). Open targets platform: new
developments and updates two years on. Nucleic Acids Research 47 (D1): D1056–D1065.
25 Wang, L., Ma, C., Wipf, P. et al. (2013). Targethunter: an in silico target identification tool for
predicting therapeutic potential of small organic molecules based on chemogenomic database. The
AAPS Journal 15 (2): 395–406.
26 Liu, X., Ouyang, S., Yu, B. et al. (2010). PharmMapper server: a web server for potential drug target
identification using pharmacophore mapping approach. Nucleic Acids Research 38 (Suppl. 2): 5–7.
27 Keiser, M.J., Setola, V., Irwin, J.J. et al. (2009). Predicting new molecular targets for known drugs.
Nature [Internet] 462 (7270): 175–181. https://doi.org/10.1038/nature08506.
28 Geppert, H., Vogt, M., and Bajorath, J. (2010). Current trends in ligand-based virtual screening:
molecular representations, data mining methods, new application areas, and performance
evaluation. Journal of Chemical Information and Modeling 50 (2): 205–216.
29 Wishart, D.S., Knox, C., Guo, A.C. et al. (2008). DrugBank: a knowledgebase for drugs, drug
actions and drug targets. Nucleic Acids Research 36 (Suppl. 1): 901–906.
30 Kim, S., Chen, J., Cheng, T. et al. (2019). PubChem 2019 update: improved access to chemical data.
Nucleic Acids Research 47 (D1): D1102–D1109.
31 Seiler, K.P., George, G.A., Happ, M.P. et al. (2008). ChemBank: a small-molecule screening and
cheminformatics resource database. Nucleic Acids Research 36 (Suppl. 1): 351–359.
32 Wang, Y., Zhang, S., Li, F. et al. (2020). Therapeutic target database 2020: enriched resource for
facilitating research and early development of targeted therapeutics. Nucleic Acids Research
48 (D1): D1031–D1041.
33 Chen, C.Y.C. (2011). TCM Database@Taiwan: the world’s largest traditional Chinese medicine
database for drug screening in silico. PLoS One 6 (1): 1–5.