Structure-Based Drug Discovery Service

Structure-based drug discovery is a cutting-edge approach in pharmaceutical research that utilizes the three-dimensional structural information of target proteins to design and optimize therapeutic molecules. By focusing on the molecular interactions between small molecules and their biological targets, structure-based drug discovery addresses key challenges in drug discovery, such as improving drug efficacy, specificity, and reducing off-target effects. structure-based drug discovery begins with the extraction and purification of target proteins, followed by structural determination using advanced techniques like X-ray crystallography, cryo-electron microscopy (Cryo-EM), or nuclear magnetic resonance (NMR). When experimental structures are unavailable, homology modeling is employed to generate reliable 3D structures. Active compound databases are then screened using molecular docking and structure-based virtual screening (SBVS) to identify potential binding sites. Promising candidates are synthesized, experimentally evaluated, and optimized into lead compounds. Structure-based drug discovery service includes structural determination, computational analysis, and experimental validation, providing a comprehensive solution for rational drug design. It significantly accelerates the drug discovery process, reduces costs, and increases the success rate of identifying effective therapeutics, becoming indispensable in modern pharmaceutical research. MtoZ Biolabs offers a cutting-edge structure-based drug discovery service powered by advanced technologies such as X-ray crystallography Cryo-EM and NMR and backed by years of experience and a skilled team of experts to provide precise structural insights and robust computational analysis for identifying and optimizing lead compounds and ensuring high efficiency and accuracy to support researchers in designing innovative therapeutics for complex biological challenges.

 



Batool, M. et al. Int J Mol Sci. 2019.

Figure 1. The Workflow of Structure-Based Drug Discovery

 

Service Advantages

1. High-Resolution Structural Analysis

MtoZ Biolabs leverages advanced structural biology techniques such as X-ray crystallography, cryo-electron microscopy (Cryo-EM), and nuclear magnetic resonance (NMR) to determine high-resolution 3D structures of target proteins. By integrating these capabilities, our structure-based drug discovery service can accurately identify critical binding sites and optimize lead compounds, significantly improving drug design precision.  

 

2. High-Throughput Computational Screening

Addressing the challenges of structure-based drug design, MtoZ Biolabs employs molecular docking, free energy calculations, and molecular dynamics simulations to enhance small-molecule screening strategies. Our structure-based drug discovery service integrates high-throughput virtual screening technologies to improve candidate selection efficiency, reduce experimental screening costs, and accelerate early-stage drug discovery.  

 

3. Efficient Drug Optimization  

Traditional structure-based drug design can be limited by the complexity of protein-ligand interactions. MtoZ Biolabs' structure-based drug discovery service incorporates high-sensitivity mass spectrometry techniques such as LC-MS/MS and Native MS to analyze small-molecule interactions with target proteins, improving lead compound optimization and supporting innovative drug development.

 

Case Study

1. Structure-Based Drug Design with a Deep Hierarchical Generative Model

In recent years, the expansion of crystal structure data and libraries of synthesizable molecules has opened new chemical space for drug development. Despite advancements in virtual ligand screening, computational constraints and the vastness of drug-like space still limit scalability. Deep learning approaches are overcoming these challenges by learning key intra- and intermolecular relationships in drug-target systems from existing data. DrugHIVE, a deep hierarchical variational autoencoder (VAE), outperforms existing autoregressive and diffusion-based models in molecular generation, providing enhanced control over the process. It improves virtual screening efficiency and accelerates tasks such as de novo molecule generation, molecular optimization, scaffold hopping, linker design, and high-throughput pattern replacement. Additionally, it can be applied to high-confidence AlphaFold-predicted receptor structures, extending drug design capabilities to a majority of the unresolved human proteome. Structure-based drug discovery service leverages protein structural information and deep learning algorithms to optimize small molecule-target interactions and enhance virtual screening efficiency. By employing advanced molecular generation and optimization strategies, it facilitates de novo drug design, scaffold modification, linker optimization, and high-throughput screening, supporting precision drug discovery.

 



Weller, JA. et al. J Chem Inf Model. 2024.

Figure 2. Molecular Generation Using AlphaFold-Predicted Structures

 

2. Structure-Based Drug Designing and Immunoinformatics Approach for SARS-CoV-2

SARS-CoV-2-induced respiratory illness has been rapidly spreading due to its high transmissibility and global healthcare limitations. Compared to de novo drug discovery, drug repurposing can shorten development timelines and reduce costs. This study employed virtual screening to identify antiviral compounds targeting the SARS-CoV-2 spike glycoprotein (S), main protease (Mpro), and receptor-binding domain (RBD)–ACE2 complex, revealing that PC786 exhibited high binding affinity to all targets. Furthermore, structural changes were observed in the post-fusion conformation of the trimeric S protein RBD upon PC786 binding. Immunoinformatics-based T-cell and B-cell epitope predictions were utilized to enhance the efficiency and reliability of vaccine candidate selection. Structure-based drug discovery service utilizes protein structural information and computational approaches to optimize small molecule design and improve molecular screening efficiency. By integrating virtual screening and immunoinformatics analysis, it facilitates the exploration of candidate drugs and biological targets, supporting early-stage drug development for antiviral and other disease applications.

 



Panda, PK. et al. Sci Adv. 2020.

Figure 3. Structural Basis of the RBD-ACE2 Complex Protein-Protein Interaction

 

Applications

1. Targeted Drug Design and Lead Optimization

Structure-based drug discovery service enables precise identification of key binding sites on target proteins, facilitating the rational design and optimization of lead compounds. This approach enhances drug efficacy, selectivity, and safety, reducing the time and cost associated with traditional drug discovery methods.

 

2. Development of Inhibitors for Disease-Related Proteins

Many diseases, including cancer, neurodegenerative disorders, and viral infections, are driven by aberrant protein functions. Structure-based drug discovery service aids in the development of small-molecule inhibitors that effectively modulate disease-related proteins, providing potential therapeutic solutions for complex diseases.

 

3. Antibody and Biologics Engineering

By elucidating the structural interactions between antibodies, receptors, and antigens, structure-based drug discovery service plays a crucial role in optimizing therapeutic antibodies and biologics. This approach improves binding affinity, stability, and immune response, accelerating the development of next-generation biologic therapies.

 

Deliverables

1. Comprehensive Experimental Details

2. Materials, Instruments, and Methods

3. Relevant Liquid Chromatography and Mass Spectrometry Parameters

4. The Detailed Information of Structure-Based Drug Discovery

5. Mass Spectrometry Image

6. Raw Data