The Use of Computational Tools in Ligand-Based Drug Design

Ligand-based drug design is a method of drug lead development that focuses on adapting and modifying known ligands to the biomolecular target to optimize affinity, thereby improving drug efficacy. It is the most ubiquitous strategy used by medicinal chemists in drug design and discovery when the 3D structure of potential drug targets is unknown or absent, as opposed to structure-based drug design, which begins from the known structure of the target site.

Image Credit: Gorodenkoff/Shutterstock.com

Image Credit: Gorodenkoff/Shutterstock.com

Owing to the broad range of structural and chemical changes that can be made to a drug lead while maintaining electrostatic and structural similarity and thus retaining their structure-activity relationship, computational tools are frequently exploited to assess potential leads for similarity in a high throughput manner. This article explores how computational tools are used in ligand-based drug design.

The Role of Computer-Aided Drug Design in Ligand-Based Drug Design

The first step of the general drug design process includes identifying a known compound responsible for the desired physiological effect, which may typically consist of a key protein within a significant biochemical pathway for the disease or a natural compound with a history as a therapeutic agent, for example.

Importantly, the target molecule of ligand-based drug design leads, with which the drug interacts to produce the desired effect, is unknown. Thus, design iteration begins from the best-known efficacious compound. The most promising lead compounds can then be produced in limited quantity and compared to the starting drug lead in vitro or in vivo in their ability to inhibit or promote the biochemical pathway of interest and treat the disease.

The influence of chemical and structural changes on the drug lead can be interpreted using computational tools and indicate the probable electrostatic interactions of the molecule to the target site. Unfortunately, as the target site molecule is unknown or absent during ligand-based drug design, docking simulations that directly report the affinity and stability of the drug lead-target bond cannot be performed.

However, design changes to the starting lead drug compounds, such as the exchange of functional groups or rearrangement of the molecular structure, can be explored for similarity and feasibility using computer-aided drug design (CADD).

CADD is a useful tool within rational drug design that aids with novel drug candidate identification, characterization, and structure optimization. This method can also be valuable for the rational design of prodrugs, usually designed to increase the specificity or bioavailability of the starting or novel drug molecules.

CADD methods can be applied to ligand-based drug design and are useful even when the experimental 3D structure of a target is absent. In this type of drug design, without a target structure, the known ligand molecules that bind to the biological drug target are analyzed to further aid comprehension of its structural and physicochemical properties and allow correlation with the desired pharmacological activity.

The ligand-based drug design approach can also include natural products or substrate analogs that interact with the biological target molecule and can produce the desired pharmacological effect.

Quantitative Structure-Activity Relationships

Ligand-based drug design frequently employs quantitative structure-activity relationships (QSARs) and pharmacophore models to predict drug efficacy. QSAR is a computational method that can quantify the relationship between the chemical and structural features of a range of compounds with their ultimate biological process. This method is based on the underlying hypothesis that similar structural or physicochemical properties can produce similar biological activity.

Once a group of lead molecules is identified that demonstrates the desired biological activity, a quantitative relationship can be verified between the physicochemical characteristics of the molecules and the biological activity of interest.

Subsequently, the QSAR model can be used to optimize active compounds to maximize the desired biological activity. The key chemical and structural properties of the identified active compounds can then be compared to a library of bioactive compounds, and potential drug leads selected or modifications to the starting drug lead can be tested entirely in silico.

Predicted compounds that have the potential to demonstrate the biological activity of interest are put through experiments to verify and test the desired activity wanted by researchers for drug development. The QSAR tool can be used as a guide for identifying compounds as well as modifying these to result in molecules with improved activity.

Case Study: 5-LOX inhibitor development

Arachidonate 5-lipoxygenase (5-LOX) is an iron-containing enzyme involved in the degradation of fatty acids, which is employed in a number of essential biological processes throughout the human body. Aberrant 5-LOX activity is implicated in various disease states, and thus, inhibition of enzyme activity by small molecule drug action is desirable. Small molecule inhibitor 5-hydroxyindole-3-carboxylate is known to inhibit this enzyme, and thus QSAR studies have been employed in identifying and designing a range of 5-hydroxyindole-3-carboxylate derivatives with potentially improved affinity.

Specifically, comparative molecular field analysis (CoMFA) and comparative molecular indices analysis (CoMSIA) were used to generate a series of derivatives, each containing two structural or chemical substitutions. The inhibitory activity of each derivative is predicted by CoMFA and CoMSIA based on structural and electrostatic similarities to the starting lead compound in a quantitative manner, producing predicted inhibitory concentration 50 (IC50) values.

Future Outlook

Ligand-based drug design can be an effective approach to lead compound development that focuses on identifying the structural and chemical features of lead drug compounds that likely play a role in their biological activity, allowing design iterations to be made without knowledge of the structure of the target site.

Delineating the structural and chemical characteristics of the ligands of a drug target can lead to a higher level of comprehension about the types of interactions the ligand is involved with and how this can aid with producing the desired pharmacological response. This approach can be used to predict novel molecular structures that will generate significant interactions with the drug target and to develop effective drug treatments for various diseases and disorders.

Ligand-based drug-design methods can complement other drug lead identification and development methods, including more typical structure-based drug-design efforts, where the target molecule is known. In this way, optimal structures predicted by CoMFA and CoMSIA methods can be tested in docking simulations that feature the structural and electrostatic features of the target, generating corroborative data prior to drug synthesis and in vitro experimentation.

Sources:

  • 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. doi:10.2174/157340911793743547
  • Sadybekov AV, Katritch V. Computational approaches Streamlining Drug Discovery. Nature. 2023;616(7958):673-685. doi:10.1038/s41586-023-05905-z
  • Umar AB, Uzairu A, Shallangwa GA, Uba S. Ligand-based drug design and molecular docking simulation studies of some novel anticancer compounds on MALME-3M Melanoma Cell Line. Egyptian Journal of Medical Human Genetics. 2021;22(1). doi:10.1186/s43042-020-00126-9
  • Aparoy P, Suresh GK, Reddy KK, Reddanna P. CoMFA and CoMSIA studies on 5-hydroxyindole-3-carboxylate derivatives as 5-lipoxygenase inhibitors: Generation of homology model and docking studies. Bioinorg. & Med. Chem. Lett.. 2011;21(1). doi:10.1016/j.bmcl.2010.10.119

Further Reading

Last Updated: Sep 7, 2023

Michael Greenwood

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Michael Greenwood

Michael graduated from the University of Salford with a Ph.D. in Biochemistry in 2023, and has keen research interests towards nanotechnology and its application to biological systems. Michael has written on a wide range of science communication and news topics within the life sciences and related fields since 2019, and engages extensively with current developments in journal publications.  

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