Structure-Based vs Ligand-Based Drug Design

Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD) are the two main approaches in drug development. Structure-based drug design is the design of molecules that can bind to the protein based on the three-dimensional structural information of the target protein (such as obtained by X-ray crystallography or nuclear magnetic resonance techniques). This method is suitable for situations where the structure of the target is known, and can directly optimize the molecule to match the binding site of the target precisely. Ligand-based drug design is based on information about small molecules (ligands) that are known to bind to the target. When the structure of the target protein is unknown, the method predicts and designs compounds with similar activity by analyzing the chemical properties and mechanism of action of existing ligands. Common techniques include molecular docking and pharmacophore modeling.

You can simply understand that SBDD relies on the detailed structure of the target protein, while LBDD uses information from existing ligands to guide the development of new drugs. After understanding the basic concepts, you will be able to distinguish the two approaches in detail below.

Figure 1: Drug discovery flow chart (Grey, et al.,2010)

Introduction to Structure-Based Strategies

Structure-based drug design (SBDD) is a general term for drug design based on the Structure of ligands and receptor proteins. In a narrow sense, it is the drug design based on the receptor structure, that is, according to the three-dimensional structure of the target of drug interaction (generalized receptors, such as enzymes, receptors, ion channels, antigens, nucleic acids, polysaccharides, etc.), the principle of molecular recognition (complementarity) is used to design the lead compound for receptor regulation. Or according to the existing drug force and structure-activity relationship to predict the efficacy of new compounds, to achieve the purpose of discovering active molecules.

The main process of structurally based drug design includes target protein structure analysis, binding site analysis, molecular design and optimization, and in vitro validation. By obtaining high-resolution protein structures using techniques such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy, researchers can identify drug-binding sites and design highly specific small-molecule drugs in combination with methods such as molecular docking and kinetic simulation. SBDD is suitable for situations where the target structure is known or resolvable, and has significant advantages of improving the accuracy of drug design, accelerating the development process, and reducing side effects, while also playing an important role in the discovery of new targets and drug optimization.

Structure-Based Drug Design

Structure-based drug design is a method to design or optimize small molecule compounds that can bind to the target protein by analyzing the spatial configuration and physicochemical properties of the binding site based on the three-dimensional structure information of the target protein. Its core idea is "structure-centric", which optimizes drug candidates through molecular docking and simulation.

Key Techniques in Structure-Based Drug Design

Because the SBDD process is most powerful when it begins with the atomic-resolved structure of the target, reliable, fast, and predictable methods must be developed to obtain this information in order to develop highly effective drug candidates. Structural analysis of protein targets can be obtained by X-ray crystallography or nuclear magnetic resonance (NMR) techniques. However, most of the protein targets used in SBDD were obtained using X-ray crystallography.

In the article Molecular Docking in Drug Discovery, we have briefly introduced that these methods can help improve the accuracy of protein structure, and the application of this technology in structure-based drug design will be emphasized in the following.

X-ray Crystallography

X-ray crystallography is a method of determining the three-dimensional structure of protein crystals by analyzing the diffraction patterns produced by them under X-ray irradiation. The basic principle is to expose a protein crystal to a beam of X-rays, which interact with a cloud of electrons in the crystal to create a diffraction pattern. Using mathematical algorithms, such as the Fourier transform, the three-dimensional structure of the protein can be reconstructed from the diffraction data. This method is often used to resolve proteins with relatively stable structures that are easy to crystallize. Protein structures obtained by X-ray crystallography can help researchers identify drug binding sites and design drug molecules with high affinity on this basis, further improving the activity and selectivity of drugs.

A classic example is the breakthrough in X-ray crystallography that has produced high-resolution structures of more than 30 GPCRS, providing a structural basis for drug design and functional studies. These enable computational GPCR research methods, which have led to several groundbreaking studies over the past few years.

Nuclear Magnetic Resonance (NMR)

Nuclear magnetic resonance (NMR) is a method of studying the structure, dynamics, and interactions of molecules by measuring the magnetic reactions of atomic nuclei. NMR can provide detailed information about the state of molecules in solution, including the distance between atoms, angles, and the movement and conformational changes of molecules. Unlike X-ray crystallography, NMR does not require protein crystallization, so it is particularly suitable for proteins that cannot form crystals, especially those whose structures are flexible and dynamically changing.

In drug design, NMR is used to resolve the interactions between drug molecules and target proteins. For example, in antiviral drug development, NMR technology is used to study how drug molecules bind to HIV reverse transcriptase, providing key structural information for optimizing inhibitors. Especially in the absence of high-quality crystals, NMR technology presents a unique advantage in providing more intuitive and real-time dynamic information for drug design.

Cryo-EM

Cryo-electron microscopy (Cryo-EM) is a rapidly developed analytical technique in recent years, which can directly observe the three-dimensional structure of macromolecular complexes at close to atomic resolution. Unlike traditional X-ray crystallography, Cryo-EM does not require protein crystallization and is suitable for complex biomacromolecules that are difficult to form crystals, especially membrane proteins, viruses, and multiprotein complexes. By cryo-electron microscopy, high-resolution three-dimensional images of protein complexes can be obtained without destroying the structure of the molecule itself.

Cryo-EM is used to study G protein-coupled receptors (GPCRS) and their interactions with drugs, providing important data for drug design against cardiovascular and neurological diseases.

Advantages of Structure-Based Design

One of the core advantages of structure-based drug design is the most accurate targeting **. By analyzing the three-dimensional structure of the target protein in detail, the researchers were able to identify the binding site between the drug molecule and the target protein, which in turn provided a scientific basis for drug design. This fine targeting allows the drug to bind to the target efficiently at the molecular level, thus greatly improving the activity and therapeutic effect of the drug. Further, structure-based drug design can also optimize the binding pattern of drug molecules to the target, so that the drug exhibits higher affinity and stability when binding to the target. As a result, drug molecules can interact with specific targets more precisely, avoiding binding to non-target proteins, reducing off-target effects, and significantly reducing the occurrence of side effects. This highly selective design not only improves the efficacy of the drug, but also ensures a safer therapeutic effect in clinical applications, thus providing strong support for the development and optimization of new drugs.

Peptide drugs are often more difficult to pass through molecular docking and computer analysis because peptide structures are more flexible, have complex molecular conformations, and undergo complex interactions. A classic example of the development and application of a structure-based design strategy is the cancer-targeting peptide for GRP78, and several optimized helical peptides have been reported to exhibit PM-NM protein-binding affinity.

Limitations and Challenges

Obtaining high-quality structures of target proteins remains a major challenge in structurally based drug design. Although modern structural analysis techniques such as X-ray crystallography, nuclear magnetic resonance (NMR), and Cryo-EM (cryO-EM) have made significant progress in the past few years, structural analysis remains difficult for some proteins that are difficult to crystallize, especially membrane proteins, large complexes, or proteins with highly flexible and dynamic characteristics. Even if the three-dimensional structure of the protein can be successfully obtained, some proteins may exhibit different conformations under different experimental conditions, which makes it more complicated to accurately predict the drug binding site, especially in the case of conformational transformation of these proteins, the drug binding mode may also change, further increasing the difficulty of design.

In addition, although computational methods such as molecular dynamics simulations and molecular docking provide powerful tools for drug design, the accuracy and effectiveness of these computational methods depend on several factors, including the quality of the protein structure and the precision of the simulation algorithms. Current computational methods still face certain limitations, such as the fact that the computational power for large-scale systems is not fully mature enough to handle complex protein-drug complexes containing large numbers of atoms. In addition, the complexity of the biological environment during the simulation, such as solvent effects, ion concentrations and protein dynamics, may also affect the predicted results. Therefore, although computational simulations can provide important design clues, the optimization process of drug molecules still needs to be combined with validation of experimental data to ensure their actual performance and efficacy in biological systems.

Ligand-Based Drug Design

Techniques in Ligand-Based Drug Design

Quantitative Structure-Activity Relationship (QSAR)

Quantitative Structure-Activity Relationship (QSAR) is a technique for analyzing quantitative relationships between chemical structures and biological activity based on mathematical models. By extracting molecular characteristics of compounds (such as electronic properties, hydrophobicity, stereo structural parameters, etc.), QSAR models can predict the biological activity of compounds and help researchers screen for molecules with potential drug effects. Specifically, researchers build mathematical models based on data on the structure and activity of known compounds, and then use this model to predict the activity of new compounds, thereby screening out candidate molecules in a short period of time.

Pharmacophore Modeling

Pharmacophore Modeling is a method of modeling a range of compounds known to be active by extracting common features (such as receptor-binding groups, charge distribution, spatial arrangement, etc.). Pharmacophore models describe key features necessary for the interaction of a compound with a target protein and provide guidance for the design of new active molecules. Even when the structure of the target is unknown, the method can be used for molecular screening by information on known active compounds.

Virtual Screening

Virtual screening is a method that uses computer simulations to screen a library of compounds to quickly identify potentially active molecules by predicting potential interactions between a compound and a target protein. Virtual screening is often combined with QSAR models or pharmacophore modeling to improve screening efficiency and accuracy. Compared with traditional experimental screening, virtual screening can handle a large number of compound databases and greatly improve the screening speed.

Advantages of Ligand-Based Design

Ligand-based drug design has significant advantages. First of all, it does not require the target structure, that is, it does not depend on the three-dimensional structure of the target protein, so it is suitable for situations where the target structure is difficult to parse, especially when the target protein is difficult to crystallize or is understudied. This allows researchers to design drugs even when information about the target is incomplete. Secondly, ligand-based design can significantly save resources, by using structural information of known active molecules to rapidly screen potentially active compounds, thus significantly reducing the time and cost of experimental screening. For example, using techniques such as QSAR models or pharmacophore modeling can efficiently identify compounds with potential and focus resources for further validation and optimization. Finally, ligand-based design is not limited to the study of known targets, but can also help discover new target proteins or biological pathways by analyzing the mechanism of action of active molecules, exploring unknown targets. This kind of exploratory research provides innovative directions for the development of new drugs and broadens the vision of drug research and development. Therefore, ligand-based drug design not only improves the efficiency of drug development, but also opens up new paths for drug discovery.

Limitations and Challenges

Although ligand-based drug design has many advantages, it also faces certain limitations and challenges. First, ligand-based design is highly dependent on data quality, which is centered on predicting the activity of a compound by building a mathematical model, and the quality of the model depends on the amount and accuracy of the input data. If there is insufficient data on the known active molecules used to train the model, or if the data is biased, the predictive power of the model will be greatly compromised and may lead to inaccurate screening results. Second, the predictive accuracy of ligand-based designs is limited, because the method usually lacks direct structural information of the target protein and cannot accurately predict the specific mode of interaction between the compound and the target. Without a full understanding of the interactions between molecules, predicted compounds may not perform as well as expected in experiments, adding uncertainty to the drug development process. Finally, model deviation is also a problem that cannot be ignored. Although existing QSAR models and pharmacophore models are effective in screening potential drugs, they may not be able to fully describe complex biological systems. For example, there may be multi-target action, dynamic protein conformational changes, and the influence of environmental factors in biological systems that are often beyond the predicted range of current models. Therefore, in order to compensate for these shortcomings, ligand-based design still needs to be closely integrated with experimental data to ensure the reliability of results and improve the success rate of drug development.

Comparison Between Structure-Based and Ligand-Based Drug Design

Choosing the Right Approach: Structure-Based vs Ligand-Based Design

Structure-based drug design is a technique to design drug molecules based on the three-dimensional structure of the target protein, which is suitable for the situation where the target structure is clear and the binding site is clear. In this approach, researchers first parse the structure of the target protein and then precisely design drug molecules that can bind to the target. The advantage of structure-based design is that it can provide higher design accuracy and help optimize the binding pattern of drug molecules to target proteins, so it is very suitable for the development of drugs with known targets, especially when the three-dimensional structure and binding site of the target protein are well defined. In addition, structure-based drug design is particularly suitable for the development of new target drugs, which can fine-tune the structure of drug molecules through target structure analysis and molecular docking techniques to achieve the best efficacy and selectivity. In this way, the binding of the drug to the target protein can be more targeted, thereby reducing side effects and improving the therapeutic effectiveness of the drug.

Unlike structure-based design, ligand-based drug design does not rely on the three-dimensional structure of the target protein, but designs new drug molecules by analyzing the structural characteristics of known active molecules (such as pharmacophore, molecular interaction patterns, etc.). This makes it possible to screen and design drugs based on existing ligand information when the target structure is unknown or difficult to obtain. Therefore, ligand-based drug design is well suited for use when the target structure is unavailable or unknown, especially in the early drug discovery phase, where a library of known active molecules or compounds can be used for rapid screening to find potential drug candidates.

The advantage of ligand-based design is its flexibility and efficiency, which enables drug design using quantitative relationships between chemical structure and biological activity (such as QSAR models) or pharmacophore characteristics without information about the structure of the target protein. It also provides more potential target molecules for drug discovery, especially when the target is not well understood.

Hybrid Approach: Combining Both Methods

With the continuous advancement of drug development technology, more and more researchers have begun to combine structural basis and ligand-based design, adopting a hybrid approach to give full play to the advantages of both. By combining the target structure and ligand information, the hybrid method not only uses the detailed site information provided by the target structure to guide the drug molecule design, but also optimizes the biological activity of the compound through the ligand information, thus improving the accuracy and efficiency of drug design.

Overview of hybrid methods: In hybrid methods, drug design usually first relies on three-dimensional structural analysis of the target, accurately locate the binding site of drug molecules and targets through molecular docking and other technologies, and design preliminary drug candidate molecules. Subsequently, ligand-based design methods (e.g., QSAR model, pharmacophore modeling, etc.) are used to further optimize these preliminarily designed compounds to improve their bioactivity, selectivity, and drug metabolism properties. In this way, the designed drug molecules can not only precisely bind to the target protein, but also fully consider the metabolic and pharmacodynamic properties in the organism, improving the success rate of drug candidates.

Application example: In the research and development of anticancer drugs, the hybrid method has been widely used. For example, researchers can first analyze the three-dimensional structure of the target protein through X-ray crystallography or Cryo-EM techniques, and combine structural analysis to design binding sites of drug molecules. Next, the preliminarily designed compounds are optimized using ligand-based methods such as QSAR models and pharmacophore modeling to improve their binding affinity and biological activity to the target. This strategy of combining target structure and ligand information has significantly improved the efficiency of drug development and shortened the development cycle of new drugs.

Advantages: The hybrid approach combines the advantages of structure-based design and ligand-based design to further optimize the properties of the compound through ligand information while ensuring target specificity. In this way, drug development can not only improve success rates, but also save significant resources and time. For example, target structures provide precise targeting and binding patterns, while ligand-based screening methods enable rapid detection and optimization of potential drug molecules at an early stage. Therefore, the hybrid approach provides a more comprehensive and flexible strategy for drug development, which greatly improves the efficiency and success rate of new drug development.