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In the second step, ligands (Figure 3.1) are compiled from various sources to form
a library, and these sources range from commercial databases and internal libraries
to natural items. Since this library may contain anywhere from tens of thousands to
millions of tiny molecules, it would be computationally impossible to attach each and
every one of them to the target protein. The third step in SBVS involves docking the
ligands to the target protein. This is typically done with the aid of molecular docking
software, which predicts the most likely binding mode of the protein’sligands.The
interaction energy between the ligand and the protein is calculated by the docking
software using an energy function [7], and this information is then used to rank the
ligands in order of their predicted binding affinities.
The fourth stage of SBVS involves assig ning scores to the ligands based on their
predicted binding affinities. The free energy of binding of the ligand to the protein is
calculated using the scoring function [13], which takes into account multiple factors
including covalent and ionic bonds, van der Waals interactions, and hydrogen bonds.
The final step of SBVS is to verify the results of the screening process. Experi-
ments are conducted to determine whether or not the top-ranked ligands actually
have the desired biological activity. This is where we check the reliability of our dock-
ing and scoring methods for use in the VS procedure.
There are numerous benefits of using SBVS in the pharmaceutical industry. It facili-
tates rapid screening of large chemical libraries, reduces the time and money re-
quired for experimental screening, and can provide insight into the mechanisms by
which ligands bind to their targets. SBVS has limitations, such as the accuracy of dock-
Figure 3.1: Outline of the structure-based virtual
screening process.
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ing and scoring methodology used and the complexity of target protein–ligand inter-
actions, which must be considered [14].
Ligand-based VS: Drug candidates can be found through ligand-based VS (LBVS) by
comparing their structure with respect to known ligands that bind to a specific pro-
tein target. The 3D structure of the target protein is not necessary for this type of VS,
which instead focuses on the characteristics of known ligands [15].
The LBVS procedure usually consists of several steps, such as molecular symme-
try, choice of features, similarity assessment, hit validation, and ligand library crea-
tion. The first step in LBVS is to create a library of ligands, which can be done with
the help of various tools [16], such as online databases or in-house chemical archives.
In the second step, the ligands in the library are brought into molecular alignment. To
achieve this, the ligands are arranged in groups based on the pharmacophoric fea-
tures they share, such as the presence of aromatic rings, hydrophobic groups and pro-
ton donors, and acceptors [17]. The third step of LBVS, “feature selection,” involves
focusing on the most important pharmacophoric features necessary for ligand bind-
ing. Methods such as principal component analysis and partial least squares regres-
sion can be used. The fourth step of LBVS involves assigning scores to the ligands in
the library based on how closely they resemble known ligands. The Tanimoto coeffi-
cient and the Euclidean distance are two common similarity metrics used to deter -
mine structural similarities between ligands.
The final step of LBVS is to verify the hits generated by the screening process. Ex-
periments are conducted to determine whether or not the top-ranked ligands actually
have the desired biological activity. At this point, it is crucial to verify the precision of
the LBVS approach used for the VS [18].
To find novel ligands that are distinct from known ligands in terms of their scaf-
folds and to find ligands with different modes of action, LBVS makes it possible to
screen large compound libraries without prior knowledge of the structure of the tar-
get protein. Important drawbacks of LBVSincludetheneedforalibraryofwell-
characterized recognized ligands and the likelihood of false positives due to the struc-
tural similarity of ligands [19].
Validation, applications, and current trends: Validation in VS refers to checking
that the computational methods used to envisage the binding ability of small com-
pounds to a specific protein are accurate and reliable. For this reason, VS becomes
one of the crucial steps to ensure that the highest-ranked compounds recognized as
probable candidates can actually bind to the target protein and have the required bio-
logical activity [20].
Validation of VS procedures can be accomplished in a number of ways, including
through the use of cross-validation, prospective, and retrospective validation. Retro-
spective validation assesses the method’s accuracy relative to a dataset of previously
characterized protein–ligand complexes, while prospective validation employs a new
set of protein–ligand complexes to test its efficacy. Cross-validation refers to testing
3 Virtual screening tools in ligand and receptor-based drug design 55
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the procedure on multiple datasets to establish its robustness and generalizabil-
ity [21].
VS is useful in drug discovery because it allows the efficient and cheap screening
of large compound libraries, the prioritization of compounds for experimental testing,
and the disclosure of information about the mechanism that governs the binding of
ligands to the target protein. Constraints of VS include the precision of the computa-
tional methods employed, the complexity of protein–ligand interactions, and the need
for experimental validation of the lead compounds [22].
Research in VS: VS techniques have become an important part of the drug discovery
process as they allow researchers to identify potential drug candidates more quickly
and cost-effectively than traditional experimental methods. By using computer simu-
lations to predict the interaction of small molecules, such as drug candidates, with
target proteins, VS can identify compounds with a high likelihood of being effective
drugs. VS research is now being done in the following areas:
1. Machine learning: Machine learning algorithms are being increasingly used to im-
prove the accuracy of VS predictions. Neural networks, random forests, and support
vector machines are all popular machine learning techniques that can be applied to
VS. These algorithms work by training on large datasets of kn own protein–ligand
complexes. This approach can significantly speed up the VS process, as it can quickly
eliminate compounds that are unlikely to bind to the target protein, leaving only the
most promising candidates for further study [23].
De novo design: De novo design aims to discover novel bioactive compounds that can
simultaneously satisfy a range of crucial performance criteria, such as activity, selectiv-
ity, physicochemical properties, and ADME-toxicity attributes, as outlined in reference
[24]. A plethora of methodologies and software applications have been i ntroduced.
However, the utilization of de novo design in drug discovery has not been extensively
adopted. This is partially associated with the synthesis of compounds that are challeng-
ing to access. The domain has experienced resurgence in recent times, owing to ad-
vancements in the realm of artificial intelligence. A noteworthy methodology is the
utilization of a variational autoencoder comprising of a pair of neural networks,
namely an encoder and a decoder network. The encoder network transforms the chem-
ical components delineated by the SMILES notation into a valid fixed-dimensional vec-
tor. According to reference [25], the decoding module has the ability to transform latent
space variables into active compounds. The utilization of an in silico model facilitated
the exploration of optimal solutions within a subspace, while the decoder networks lev-
eraged this functionality to perform the inverse translation of said matrices into tangi-
ble molecules. In the vast majority of reverse translations, a single molecule tends to
dominate, with minor structural modifications occurring less frequently. The latent fea-
ture model was utilized by the researchers to train a system on the QED drug-likeness
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rating and the synthetic accessibility score (SAS). The feasibility of generating a se-
quence of chemical compounds exhibiting precise and targeted characteristics is a plau-
sible prospect. The antagonistic learning algorithm utilizes a generating model to
produce distinct chemical structures. The primary objective of the predictive model is
to deceive the discriminative model, while a secondary discriminative adversarial
model is concurrently trained to differentiate authentic molecules from those that are
artificially generated. The results obtained from the antagonistic algorithm in generat-
ing mode indicate a higher number of acceptable architectures in comparison to the
variational learning algorithm. An in silico approach can be employed to generate
novel structures that are expected to exhibit efficacy against the dopaminergic type 2 re-
ceptor [26]. Kadurin et al. employed a generative adversarial network (GAN) to propose
chemical compounds that exhibit potential anticancer properties. Several distinct archi-
tectures have recently been developed, each capable of generating complete and effec-
tive novel structures. One such architecture is the recursive neural network (RNN). The
aforementioned methodologies can be employed to investigate an unexplored chemical
domain, whereby the resultant molecular properties exhibit resemblances to those of
the reference space. The initial application of the methodology demonstrated promising
results, as 80% of the molecules displayed the expected activity. However, additional
proficiency pertaining to the enormity of the chemical space analyzed and the chemical
soundness of the designated compounds is necessary [27].
The planning of synthesis: The process of synthesizing molecules is a challenging
task in the field of organic chemistry. Chemists typically rely on their experience and
employ a repetitive and time-consuming problem-solving approach, often leading to
suboptimal outcomes. The potential utility of artificial intelligence and machine learn-
ing has been demonstrated in small-molecule predictive chemistry and synthetic plan-
ning domains. Some commercial entities have reported incorporating in silico synthetic
planning as a component of their overall strategy for acquiring target molecules [28].
Although chemistry is often perceived as a traditional science that is resistant to
change, there is growing interest in exploring the applications of artificial intelligence
(AI) within the field. This trend is evident across various subdisciplines of chemistry.
The application of computational methods has been a longstanding practice in the field
of medicinal chemistry. These methods are often augmented by computer-aided drug
design as well as cheminformatics, with the aim of facilitating the identification and
refinement of biologically active compounds. The process of drug discovery heavily re-
lies on the crucial component of synthesis planning. Recent computational methodolo-
gies encompass various aspects such as the ability to forecast reaction outcomes based
on a specified set of inputs, predict the yield of chemical reactions, and incorporate ret-
rosynthetic planning [29]. Retrosynthetic planning is predominantly dominated by
knowledge-based systems that rely on rules derived from expert knowledge or reaction
datasets. A recent study employed diverse computer strategies to predict future synthe-
sis. Routes are evaluated according to the retrosynthesis methodology. One approach
involves the integration of chemical descriptors derived from quantum mechanics with
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a combination of manually programmed rules and machine learning algorithms to pre-
dict the outcome of a reaction and its resulting products. This methodology is employed
to predict the result of a multistage analytical reaction [30].
In quality control and quality assurance: The manufacturing process of a desired
product via raw materials necessitates the consideration and balancing of multiple
factors. Product quality control tests and batch-to-batch uniformity necessitate man-
ual intervention. This underscores the imperative for the implementation of artificial
intelligence at this particular level. The proposed “quality by design” plan by the Food
and Drug Administration aimed to enhance comprehension of crucial operations and
specifications that dictate the ultimate quality of pharmaceutical products, leading to
modifications in the current good manufacturing practices (cGMP). In their study,
Gams et al. utilized a combination of human and artificial inte lligence methodologies
to analyze initial data obtained from manu facturing batches, ultimately resulting in
the development of regression trees [31]. Subsequently, said principles were trans-
formed into a set of guidelines, which were evaluated by the staff to facilitate the
management of the production process. Goh et al. conducted an investigation on the
dissolution rate of certain drugs, which is a metric for assessing the consistency of
drug batches, utilizing artificial neural network (ANN) [32]. AI has the potential to reg-
ulate production processes in real time, ensuring that the desired product specifica-
tions are met. An ANN is utilized to monitor the freeze-drying process, incorporating
a combination of self-adaptive progression, local search, and backpropagation techni-
ques. The proposed method has the potential to estimate the temperature and thickness
of desiccated cake at a future time point (t + Dt) under various operating conditions.
This could aid in conducting quality assessments of the final product. The implementa-
tion of an automated information input platform, that includes an electronic lab note-
book, in combination with sophisticated and intelligent procedures [33], can guarantee
the quality control of the product. The total quality management inference engine can
employ data collection and diverse knowledge discovery methodologies as valuable
strategies for making intricate decisions and innovating intelligent quality management
technologies.
VS-based advanced applications: The integration of AI into drug development has
the capacity to revolutionize the current duration and extent of pharmaceutical inves-
tigation. AI does not depend on preestablished targets in the context of drug discov-
ery. Consequently, the medication development process is devoid of any subjec tive
bias or preexisting information. AI employs the latest advancements in the fields of
biology and computation to develop state-of-the-art algorithms for drug discovery. AI
possesses the capability to equalize opportunities in drug research, owing to the swift
advancements in computing capacity and reduced processing expenses. AI exhibits
superior predictive capabilities in delineating pertinent interactions in drug screening.
Through meticulous parameterization of the aforementioned test, the likelihood of erro-
neous positive results can be mitigated. One of the most significant advantages of AI is
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its potential to relocate drug screening from physical laboratories to virtual ones, result-
ing in faster production of findings and prioritization of promising targets, without the
need for extensive experimental input or personnel hours. The following are some of
the advanced applications of AI in the drug development process.
Micro/nanorobot-targeted drug delivery system: The field of micro/nanorobots has
garnered significant attention in contemporary research. The field of medical therapy
holds promise for the utilization of this technology in various applications such as tar-
geted drug delivery, surgical procedures, disease diagnosis, and other related areas. In
contrast to the conventional approach of drug administration that depends on the
bloodstream for transportation to the intended site, the suggested micro/nanorobots ex-
hibit self-directed mobility, thereby facilitating the delivery of pharmaceutical agents to
anatomically challenging regions. The use of nanotechnology has the potential to en-
hance the solubility of medication, alter drug distribution across various tissues and or-
gans, regulate release rates to achieve sustained and controlled release patterns, and
prompt drug aggregation in the intended target [34]. Nanorobots consist predominantly
of integrated circuits, sensors, power sources, and secure d ata backup mechanisms,
which are sustained through computational technologies, including AI. The entities in
question have been designed with the capability to evade collisions, recognize and pin-
point targets, initiate attachment, and subsequently expel from their physical form. Re-
cent developments in nano/microrobotics have facilitated their ability to navigate
towards specific regions within the body, guided by physiological parameters such as
pH. This advancement has resulted in improved efficacy and reduced systemic side ef-
fects. Microchip implants are employed for the purposes of programmed release and
detection of the implant’s location within the body [35].
In nanomedicine: Nanomedicines are a class of medical interventions that integrate
the principles of nanotechnology and medicine to facilitate the diagnosis, treatment,
and monitoring of various diseases, including but not limited to cancer, HIV, malaria,
and a range of inflammatory conditions [36]. In recent years, the application of nano-
particle-modified drug delivery has gained increasing relevance in the areas of thera-
peutics and diagnostics due to its improved efficacy and therapeutic potential. The field
of nanomedicine has advanced to investigate multifunctional strategies that enable the
simultaneous integration of therapeutic and diagnostic agents onto a single particle, or
the delivery of multiple nanomedicine-functionalized therapies in a coordinated man-
ner. The implementation of these techniques has the potential to enhance treatment ef-
ficacy by focusing on the distribution of multiple agents while maintaining medication
synergy, which aligns with the objectives of conventional combination therapy [37]. The
investigation of nanomedicine-based delivery systems for drugs is often conducted at
fixed dosages, akin to conventional or unmodified combination therapy. The integra-
tion of nanotechnology and AI has the potential to address a range of challenges en-
countered in the development of formulations. The development of silicasomes,
comprising of multifunctional mesoporous silica nanoparticles loaded with irinote-
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can and iRGD, a tumor-penetrating peptide, was facilitated by the use of AI. The use
of iRGD has been observed to enhance the process of silicasome transcytosis, leading
to a notable increase of three- to fourfold in the uptake of silicasomes. This improve-
ment has been associated with favorable treatment outcomes and an overall in-
crease in survival rates [38].
Deep learning: Deep learnin g technologies, such as deep n eural networks, are
being increasingly applied to VS to improve the accuracy of predictions. These algo-
rithms can learn from large datasets of protein–ligand complexes and capture com-
plex nonlinear relationships between features, allowing them to better mimic the
intricate interactions between proteins and ligands [39].
2. Fragment-based Screening: Fragment-based screening is a VS technique that has
gained popularity in recent years. Instead of screening larger, more complex com-
pounds, this approach tests small fragment-like compounds for their ability to bind to
the target protein. This strategy allows for finding of novel ligands with different scaf-
folds from those of existing ligands, potentially leading to the development of new
classes of drugs [40].
3. Hybrid techniques: Hybrid VS techniques combine the strengths of both ligand-
based and structure-based approaches to improve the accuracy and effectiveness of
VS. LBVS relies on the known properties of active ligands to identify new compounds
that are likely to bind to the target protein. In contrast, the methodology of SBVS in-
volves the utilization of the three-dimensional structure of the target protein for iden-
tifying potential binding sites and forecasting the binding affinity of molecules that
are capable of fitting into stated sites. Hybrid VS techniques aim to combine the ad-
vantages of both approaches by integrating ligand-based and structure-based features
in a single algorithm [41].
4. Quantum mechanics: These techniques are a powerful tool for predicting the elec-
tronic structure and energetics of interactions between proteins and ligands. These
methods are based on the principles of quantum mechanics, which describe the be-
havior of subatomic particles and the interactions between them. Quantum mechan-
ics-based techniques are computationally expensive, in that they require a lot of
computational power and time to perform calculations. However, they can provide
more precise predictions of the binding affinity of ligands to the target protein com-
pared to other methods [42].
5. Cloud-based VS: Cloud-based VS is a novel method for accelerating the VS procedure
by leveraging the power of cloud computing. In traditional VS, researchers need to
run complex computational algorithms on their local machines or on specialized
hardware, which can be expensive and time-consuming. With cloud-based VS, re-
searchers can upload their data and computational tasks to cloud computing plat-
forms, which can then distribute the workload across a large number of virtual
machines [43, 44].
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To improve computational approaches to drug discovery and speed up the develop-
ment of novel therapies for a wide range of diseases, researchers are now focusing
on VS.
Future prospective: VS has already proven useful in drug development, and the field
has many promising future directions to explore. Future possibilities for VS include:
1. Personalized medicine: In personalized medicine, individual patient data is used
to develop unique therapeutic plans. VS can be used to locate drug candidates
that are specific to a patient’s ailment based on their genetic and molecular char-
acteristics [45].
2. Drug repurposing: VS can be used to find approved medicines or compounds
with well-known safety margins for novel therapeutic purposes based on their
ability to bind to a specific target protein. This method can speed up the drug dis-
covery process and reduce the overall cost of medication development [46].
3. Big data analysis: VS can make use of big data analysis to identify potential drug
targets and candidates. With this approach, large datasets of biological and chem-
ical data are analyzed using advanced computational algorithms to identify pat-
terns, trends, and connections that can be used to create new drugs [47].
4. Multitarget VS: Multitarget VS is a computational approach that involves simulta-
neously screening multiple target proteins using compound libraries to identify
potential drug candidates that have the ability to bind to and modulate the activ-
ity of multiple targets [48]. By targeting multiple proteins involved in different
diseases or disease pathways, multitarget VS has the potential to identify drug
candidates with the ability to treat multiple diseases simultaneously or address
the complex molecular mechanisms underlying certain diseases. This approach
can significantly speed up the drug discovery process by enabling researchers to
screen large compound libraries againstmultipletargetsinasinglecomputa-
tional experiment [49].
5. Cloud-based VS: Cloud-based VS is a relatively new strategy that leverages cloud
computing resources to accelerate the VS process. By enabling researchers to use
high-performance computing resources hosted on cloud servers, this approach
can significantly reduce the time and cost of VS by eliminating the need for ex-
pensive hardware and software [50].
3.3 Conclusion
VS is a rapidly growing field with a lot of potential in drug development. With the
advancements in computational power and the development of new techniques, VS is
becoming more precise and effective in identifying potential drug candidates. This
technology has the potential to speed up drug discovery and personalized treatments,
3 Virtual screening tools in ligand and receptor-based drug design 61
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leading to more efficient and effective therapies for various diseases. As research con-
tinues in this field, we can expect to see even more exciting developments and break-
throughs in the future.
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