INTRODUCTION:

The terms "in vivo" and "in vitro" are more often used in biology, whereas the term "in silico" refers to computer-based testing.¹ Computational pharmacology, also referred to as in silico pharmacology or computational therapeutics, is a rapidly expanding field that involves the development of based on software methods for collecting, processing, and synthesizing biological and medical data from an extensive variety of sources.²

It describes, more precisely, how to use this data to build computational models or simulations that, in turn, can lead to discoveries or progress in medicine and therapies through the generation of hypotheses, predictions, and other outcomes. Computer models and computational techniques are employed "in silico drug design," or Computer aided drug design (CADD), to identify drug-like compounds utilizing bioinformatics tools.³ Physiochemical characteristics and biological activity of possible drug candidates are estimated and analysed using in silico methods. Proteins can be divided using in-silico techniques based on their structures and functions. This can be useful in situations where traditional drug development and discovery methods are too risky or take a long time. These methods include preclinical and clinical trials, lead compound discovery and optimization, target identification and validation, and so on⁴. A new medication's estimated cost to reach the market has risen to $1.8 billion USD in recent years, and up to 96% of drug candidates are lost in the process. This high attrition rate is caused by inadequate drug efficaciousness as well as insufficient drug absorption, distribution, metabolism, excretion, and toxicity. To assess drug safety, including toxicity and side effects, in vivo and in vitro methods are typically used⁵. ADME-Tox assessments have increased due to current advances in vitro models, with the organ-on-chip technique. These methods are still costly, laborious, and time-consuming. Using automated assays, high-throughput screening (HTS) methods have been developed for rapidly identifying pharmacologically active chemical compounds from large numbers of molecules. Although HTS systems minimize the need for human intervention, the application of HTS remains limited given the diversity of chemical structures. Additionally, automated instruments are costly⁶. Drug failure can be caused by a variety of issues, include poor pharmacokinetics, adverse effects, ineffectiveness, and commercial viability. Drug development and research is a very complex and time-consuming process. The application of molecular models and in silico chemistry to computer-aided drug design has recently acquired significant traction⁷. Engineering science, biology, organic chemistry, and other areas use in-silico drug design expertise.

Its advantages involve quick drug development and analysis. New drugs that are more secure and more effective are always required, but identifying and developing these drugs is an expensive, difficult, and time-consuming process⁸. Clinical trial failure rates are often high, even when ignoring challenges with target validation and hit exploration, poor pharmacokinetics, poor efficacy, and significant toxicity. There is always a demand on new medications that are more effective and have lower toxicity, but the process of discovering and developing new drugs is expensive, time-consuming, and filled with difficulties⁹. A high failure rate is frequently seen in clinical trials as a result of poor pharmacokinetics, low efficiency, and excessive toxicity, leaving aside the difficulties related to target validation and hit discovery. Wong et al.'s study, involving 406,038 studies from January 2000 to October 2015, revealed that just 13.8%of all drugs—both marketed and in development—had a favourable result. 2016 saw Dimasi and associates. An estimated $2.8 billion has been spent on research and development (R&D) for a new drug, based on data from 106 randomly chosen new drugs created by 10 pharmaceutical companies¹⁰. From synthesis until the first human testing. The FDA approval procedure needs an additional 6 to 7 years, or 80.8 months, from the start of the clinical trial. Growing risk aversion on the part of regulators such as the FDA, which has resulted in tighter safety rules, higher trial failure rates, and higher drug development costs, could be one explanation for the increase in R&D expenses¹¹. Because of this, it's critical to maximize every R&D process step. The drug discovery process begins with target identification and proceeds through target validation, preclinical and clinical development, hit identification, and lead optimization. If a medication candidate is found to be effective, it was advances development stage, then it goes through several stages of clinical trials until being submitted for approval to be introduced into the market. Compound screening tests are used to find innovative hit compounds (hit-to-lead) after the targets have been discovered and approved¹². Many strategies are available for use in Early in the process of developing drugs, features like toxicity, absorption, distribution, metabolism, and excretion (ADME) should be taken into account and optimized after hit compounds are found. One of the obstacles that frequently lead a drug candidate to fail clinical trials is an unfavourable pharmacokinetic and toxicity profile¹³. Although their differences, physical and computational screening methods are frequently employed in the drug discovery process to enhance each other and optimize screening results¹⁴. This review's first section provides a brief overview of the origins and growth of the topic known as "in silico pharmacology." This includes software-based methods and database creation, QSARs (quantitative structure–activity relationships), homology models and other molecular modeling, similarity searching, pharmacophores, machine learning, data mining, network analysis, and data analysis tools. Additionally, we have previously discussed the possible use of some of these techniques for virtual affinity profiling and virtual ligand- and target-based screening¹⁵. In this second section of the study, we will address the pharmacological space covered by some of these in silico attempts and considerably expand on the applications of these approaches to a wide range of target proteins and complex characteristics.

The methods used in SBDD are described below:

A. Target determination and structure generation:

· One of the most important steps in the drug design process is drug target identification, which determines the specific biomolecule that will be used to treat a given condition.

· Following the discovery of a viable target, SBDD works to ascertain and validate the target protein's structure.

· Both computational methods (homology modelling and protein folding) and experimental methods (NMR or X-ray crystallography) can be used to determine the target protein's structure.

· A biological object, that is typically a protein, that has the capacity to influence disease phenotypes is referred to as a therapeutic target¹⁹. Therefore, the first and most important stage in the process of developing drugs is the identification of prime drug targets.

· Traditional methods for selecting therapeutic targets include conducting studies to find genes that are expressed differentially in healthy and diseased cells or tissues, as well as proteins that have strong connections to proteins associated with the disease.

· A biomolecule that is involved in specific metabolic pathways or signals related to a disease process is called a drug target.

· The binding location, referred to as the active site, is frequently identified using X-ray crystallography or NMR (nuclear magnetic the resonance)²⁰.

Homology modelling:

The next best method to determine the protein structure is typically a homology model, if either crystallographic coordination or a 2D NMR model are available. A dimensional protein structure constructed from pieces of crystallography models is called a homology model.

Protein folding:

This technique uses protein folding to identify targets. This is a challenging procedure that begins with the basic sequence alone and attempts an enormously high number of conformers by calculation. This is an attempt to use chaperones to calculate the protein's optimal form based on the idea that the correct shape has the lowest energy. Protein folding can provide an accurate model in some cases and a relatively bad one in others.

B. Binding site prediction:

· Finding the binding location is the next stage. A protein's surface region where a ligand molecule attaches and interacts with the protein to induce a particular biological function is known as a binding site.

· Numerous in silico techniques exist for forecasting putative binding locations, including as molecular dynamics simulations, protein structure-based algorithms, and docking simulations²¹.

C. Ligand search:

· Locating possible ligands that can attach to a certain target protein or receptor is known as "ligand search²²."

· Virtual screening is a computational tool for identifying possible ligands for drug development from a huge database of chemicals. It is one of the ligand search approaches.

· In VS, a vast database of compounds that resemble drugs or lead are screened using computational techniques to find molecules that may bind to a certain target protein with high affinity and specificity²³.

D. Molecular docking:

· To predict the interactions between ligands and target proteins or between proteins themselves, a computational technique called molecular docking is utilized in drug design and screening.

· Molecular docking defines the proper binding conformation of ligands and proteins, and its goal is to find possible candidates for therapy by projecting their capacity to form a stable compound with the target biomolecules²⁴.

· Protein-protein interactions and complex affinities can be predicted and assessed using docking methods, which offers insights into the functions and roles of proteins in cells.

· In order to score the ligands according with their affinity for binding to the target protein, docking algorithms assess the ligands using scoring functions.

· A method known as "molecular docking" predicts which way molecules would orient them self to form a stable complex when coupled together²⁵.

E. Fragment- based docking:

· Using this method, molecules are divided into tiny pieces and checked to see when they have a binding affinity for the targeted receptor.

· These fragments can reveal crucial information about the major interactions that take place between the molecule and the receptor, although they often have a lower binding affinity than the complete molecules²⁶.

F. Scoring function:

· In molecular docking, scoring functions are used to evaluate the ligand-target protein binding affinity.

· For virtual screening studies to produce reliable findings, the scoring function's precision is essential.

· The majority of docking programs come with scoring tools which allow one to calculate the free energy involved in the interaction between a protein and a ligand.

· The chemical compounds collected during virtual screening studies are ranked using a variety of scoring systems, with the produced score being of the greatest significance²⁷.

G. Molecular dynamics simulations:

· One computer method for studying the physical behaviour of atoms and molecules in a system, like protein-ligand complexes, is molecular dynamics (MD) simulations.

· MD simulations employ mathematical techniques to compute the trajectories of atoms and molecules over time, taking into account their initial positions and velocities as well as the forces exerted upon them.

· This facilitates the observation of the relationships among medicines, proteins, and other molecules in a variety of environments.

· The drug-target interactions, molecular behaviour, and the structure and function of proteins can all be understood through these simulations²⁸.

2. Ligand based drug design method:

The information provided by identified inhibitors for the target receptor is used in ligand-based techniques. A variety of techniques are used to identify structures from databases that match known inhibitors. Pharmacophore matching, 3D shape matching, comparison and substructure searches are a few of the frequently used techniques. Drug design (LBDD) has reported the method's success.

A. Pharmacophore modelling:

· The pharmacophore recognizes structural characteristics shared by ligands that attach to the target, like aromaticity, acceptors, and donors of hydrogen bonds.

· The ligand's interaction with the target molecules is eventually replicated in new compounds created with these properties.

· Using pharmacophore models, compounds with similar structural and physical characteristics that could be leads in drug development are screened from compound libraries.

· Active ligands are calculated to produce energetically stable conformations, and their structures are arranged to identify similar functional groups that are shared by the active ligands in order to identify pharmacophore. This aids in discovering of possible novel therapeutic possibilities²⁹.

B. Quantitative structure-activity relationship (QSAR):

· Using structural and physicochemical characteristics, QSAR is a popular ligand-based method for drug design that creates mathematical models to predict the biological activity of chemical compounds.

· A mathematical model is created by computing molecular descriptors that represent the chemical and structural characteristics of chemicals and connecting them to biological activity.

· New drug candidates are found and lead compounds' characteristics are improved through the usage of this model.

· Compounds with good inhibitory actions and low toxicity on specific targets can be identified using this method.

· To get past the drawbacks of the older QSAR methods, a newer version known as 3D-QSAR has been created³⁰.

C. Similarity searches:

· Computational techniques called similarity searches are used to find novel compounds that have physicochemical characteristics with established active substances.

· The basic idea is that molecules with comparable characteristics are more likely to have comparable biological functions.

· The procedure involves comparing a database of chemicals with the structural and physicochemical features of known active compounds.

· Methods based on similarity are not always reliable and could overlook substances that possess distinct chemical characteristics crucial for attaching to the intended target. For the purpose of drug discovery, these approaches should therefore be combined with additional computational and experimental techniques³¹.

APPLICATIONS OF IN-SILICO DRUG DESIGN:

Some of the common applications of in-silico methods in drug design and discovery are:

· Potential candidates for therapy can be found by searching vast databases of substances via in-silico drug design. This method minimizes the amount of time and money needed for medication discovery.

· The structure of prospective drug candidates can be optimized through in-silico drug design to enhance their binding affinity, safety, efficacy, and other pharmacological characteristics.

· The binding mechanism of a drug candidate to a target protein can be predicted using in-silico methods, which makes it possible to create molecules that are more effective and selective³².

· Compounds that bind to a target protein can be designed by in-silico approaches, which make use of the chemical structure and properties of known ligands.

· Successful uses for in-silico drug design techniques have been made in the creation and identification of medicinal compounds for the treatment of a number of illnesses, such as diabetes, cancer, and bacterial and viral infections.

· The works in silico examinations demonstrated the usefulness of using Pak evaluation and drug interaction evaluation techniques in the search for novel medications and to enhance the assessed formulation³³.

· The program used for medicine creation analyses gene expression, gene sequence analysis, gene molecular modelling, and protein three-dimensional structure.

· Since silico technology may shorten the time and resources needed for drug discovery, it has become important in the current pharmaceutical business. The creation of novel therapeutic medications is an expensive and time-consuming procedure.

REFERENCES:

1. Ahlberg C. Visual exploration of HTS databases: bridging the gap between chemistry and biology. Drug Discovery Today. 1999; 4:370-376.

2. Akamatsu M. Current state and perspectives of 3D-QSAR. Top Med Chem. 2002; 2: 1381-1394

3. Albert A. Relations between molecular structure and biological activity: stages in the evolution of current concept. Ann Rev Pharmocol. 1971; 11: 13-36.

4. Chohan KK, Paine SW, Waters NJ. Quantitative structure activity relationships in drug metabolism. Curr Top Med Chem. 2006; 6: 1569-1578.

5. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (Combat). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc. 1988; 110:5959-5967.

6. Verstappen G, Reggiani A. In-silico research in drug discovery. Trends in Pharmacological Sciences. 2001; 22(1): 23-26.

7. M.A. Mehmood, U. Shearman. Ahmad Use of bioinformatics tools in different spheres of life sciences J Data Min Genome Proteonomics. 2014; 5(2); 1

8. DiMasi JA., Hansen Row, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ. 2003; 22: 151-185.

9. Wong C.H., Siah K.W., LoA, W. Estimated research and development investment needed to bring a new medicine to market, 2009-2018. JAMA. 2020; 323; 844-853.

10. DiMasi J.A., Grabowski H.G., Hansen R.W. Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ. 2016; 47: 20–33.

11. DiMasi J.A., Hansen R.W., Grabowski H.G. The price of innovation: New estimates of drug development costs. J. Health Econ. 2003; 22: 151–185.

12. Paul S.M., Matulka D.S., Dunwiddie C.T., Persinger C.C., Munos B.H., Lindborg S.R., Schacht A.L. How to improve R&D productivity: The pharmaceutical industry’s grand challenge. Nat. Rev. Drug Disco. 2010; 9: 203–214.

13. Yang Y., Adelstein S.J., Kassis A.I. Target discovery from data mining approaches. Drug Disco. Today. 2009; 14: 147–154.

14. Moffat J.G., Rudolph J., Bailey D. Phenotypic screening in cancer drug discovery—Past, present and future. Nat. Rev. Drug Disco. 2014; 13: 588–602.

15. Hart C.P. Finding the target after screening the phenotype. Drug Disco. Today. 2005; 10: 513–519. Doi: 10.1016/S1359-6446(05)03415-X.

16. Morgan P., Brown D.G., Lennard S., Anderton M.J., Barrett J.C., Eriksson U., Fidock M., Hamren B., Johnson A., March R.E. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat. Rev. Drug Disco. 2018; 17: 167–181.

17. Morgan P., Van Der Graaf P.H., Arrowsmith J., Feltner D.E., Drummond K.S., Wegner C.D., Street S.D.A. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Disco. Today. 2012; 17: 419–424.

18. Maple H.J., Garish R.A., Rigau-Roca L., Porter J., Whitcombe I., Prosser C.E., Kennedy J., Henry A.J., Taylor R.J., Crump M.P. Automated protein–ligand interaction screening by mass spectrometry. J. Med. Chem. 2012; 55: 837–851.

19. O’Reilly M., Cleasby A., Davies T.G., Hall R.J., Ludlow R.F., Murray C.W., Tisi D., Jhoti H. Crystallographic screening using ultra-low-molecular-weight ligands to guide drug design. Drug Disco. Today. 2019; 24: 1081–1086.

20. Shuker S.B., Hajduk P.J., Meadows R.P., Fenik S.W. Discovering high-affinity ligands for proteins: SAR by NMR. Science. 1996; 274: 1531–1534.

21. Madsen D., Azevedo C., Micco I., Petersen L.K., Hansen N.J.V. Chapter Four—An overview of DNA-encoded libraries: A versatile tool for drug discovery. In: Witty D.R., Cox B., editors. Progress in Medicinal Chemistry. 2020; 59: 181–249.

22. Macaron R., Banks M.N., Botanic D., Burns D.J., Cerotic D.A., Gary antes T., Green D.V.S., Hertzberg R.P., Janzen W.P., Paslay J.W., et al. Impact of high-throughput screening in biomedical research. Nat. Rev. Drug Disco. 2011; 10:188–195.

23. Hopkins AL. Network pharmacology. Nat Biotechnology. 2007; 25: 1110–1111. Doi: 10.1038/nbt1007-1110.

24. Wooller SK, Benstead-Hume G, Chen X, Ali Y, Pearl FMG. Bioinformatics in translational drug discovery. Bioscan Rep. 2017; 37: BSR20160180. Doi: 10.1042/BSR20160180.

25. Katsina T, Spirulas GA, Patrinos GP, Matsoukas MT. Computational approaches in target identification and drug discovery. Compute Struct Biotechnology J. 2016; 14: 177–184. Doi: 10.1016/j.csbj.2016.04.004

26. Dai YF, Zhao XM. A survey on the computational approaches to identify drug targets in the postgenomic era. Biomed Res Int. 2015 Doi: 10.1155/2015/239654.

27. Abadio AK, Kagoshima ES, Teixeira MM, Martins NF, Maigret B, Felipe MS. Comparative genomics allowed the identification of drug targets against human fungal pathogens. BMC Genomics. 2011; 12: 75. Doi: 10.1186/1471-2164-12-75.27

28. Shanmugam A, Natarajan J. Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification. Bioinformation. 2010; 4: 392–395. doi: 10.6026/97320630004392.

29. Lichtblau Y, Zimmermann K, Haldeman B, Lenze D, Hummel M, Leser U. Comparative assessment of differential network analysis methods. Brief Bio Informs. 2017; 18: 837–850. Doi: 10.1093/bib/bbw061.

30. Huo M, Wang Z, Wu D, Zhang Y, Qiao Y. Using expression protein interaction network analysis to identify mechanisms of dashes affecting patients with coronary heart disease. Int J Mol Sci. 2017; 18: 1298.

31. Peng Q, Schork NJ. Utility of network integrity methods in therapeutic target identification. Front Genet. 2014; 5: 12. Doi: 10.3389/fgene.2014.00012

32. Albert R, Jeong H, Bar abasi AL. Error and attack tolerance of complex networks. Nature. 2000; 406: 378–382. Doi: 10.1038/35019019.

33. Xena Rios I, Rice DW, Zalinski L, Baron MK, Marcotte EM, Eisenberg D. DIP: the database of interacting proteins. Nucleic Acids Res. 2000; 28: 289–291. Doi: 10.1093/near/28.1.29.

Received on 05.04.2024 Revised on 08.01.2025

Accepted on 13.05.2025 Published on 22.07.2025

Available online from July 26, 2025

Res.J. Pharmacology and Pharmacodynamics.2025;17(3):235-240.

DOI: 10.52711/2321-5836.2025.00038

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.