Fragment-based Drug Design

Molecular docking is mainly used to determine the optimal position and orientation of small molecules in protein targets. Although the success of this method depends on the target and software, it is also associated with binding affinity. The quality of protein-ligand interaction can be expressed to some extent by ligand efficiency (LE), that is, the average binding energy of each ligand's non-hydrogen atom. However, most studies of molecular docking predictions favor molecular binding to protein targets with detectable affinity and usable crystal structure.

Process of drug designFigure 1. Process of drug design.

Our simulation services

Project name Fragment-based Drug Design
Samples requirement
  • Molecular weight < 300
  • clogP ≤ 3
  • Number of hydrogen bond donors ≤ 3
  • Number of hydrogen bond acceptors ≤ 3
Timeline Decide according to your needs.
Deliverables We provide you with raw data and calculation result analysis service.
Price Inquiry

Compared with the screening of large molecules, fragment screening has its practical advantages:

  • It is easier to collect, maintain, and screen a library of thousands of fragments than a database of millions of macromolecules, which allows companies and academic institutions to do lead discovery;
  • A higher screening hit rate can achieve complex targets, especially those involving protein-protein interactions;
  • In addition, the fragments are small in size and high in solubility, and usually have better drug properties. The structure of these fragments can be easily optimized at later development process. The potential to become drug molecules is also greater.

Advantages of Fragment-based Drug Design

Fragment-based Drug Design-1

Screening of fragment libraries

  • Ligand Observe NMR methods.
  • Saturation Transfer Difference (STD).
  • WaterLOGSY (wLOGSY).
  • Carr-Purcell-Meiboom-Gill (CPMG).

Library preparation of client or custom libraries

  • Identity, solubility, and purity determination.
  • Automated smart pooling.
  • Positive control at regular intervals.
  • Diversity analysis.

Follow-up analysis of hits

  • Validation (follow-up of singletons).
  • Rank-order; clustering of hits.

Target Protein Generation

  • Protein synthesis and purification.
  • Target Preparation.
  • Screen design.
  • Sample optimization.
  • Experimental optimization.

Applications of Fragment-based Drug Design

Our Fragment-based Drug Design including but not limit to:

  • Feasibility assessment of new drug targets.
  • High-throughput screening and active compound discovery based on structural design.
  • Discovery of active compounds to lead compounds.
  • Optimization of lead compounds to determination of preclinical drug candidates.
  • Research on structure-activity relationship.

Our advantage

  • Computer aided drug design save a lot of labor costs.
  • Short calculation period and fast speed.
  • The funds required are far less than biological or chemical experiments.
  • High calculation accuracy.

Related services

CD ComputaBio is a professional and efficient team. The team has more than 40% of employees with master degree, doctor degree and above. Treating customers' projects CD ComputaBio is racing against time, mission must be reached, efficient and timely delivery of tasks, customer satisfaction and trust. If you have drug design needs, please feel free to contact us!

Fragment-based Drug Design FAQs

    • Q: What is the advantages of FBDD Service
      • A:One of the key advantages of FBDD is speed. By using a fragment-based approach, promising lead compounds can be quickly identified and optimized to create effective drugs. In addition, the FBDD service offers greater accuracy and precision, which means we can reduce the number of false positives and false negatives, leading to more successful drug candidates. We can customize our approach to meet your specific needs.

    • Q: What is the algorithms of FBDD Service
      • A:

        Molecular docking: By using computer simulations to model how small molecules will interact with target proteins, compounds that are likely to bind strongly to the target protein can be identified.
        Fragment Evolution: Fragment evolution can be a technique for developing small fragments into larger, more potent compounds.
        Virtual Screening: By using virtual screening, promising leads can be quickly identified and further optimized by FBDD technology
    • Q: What are the FBDD service items?
      • A: At CD ComputaBio, we offer a range of services that can help you achieve your drug discovery goals. Some of the specific FBDD services we offer include

        1. Hit Identification: Our team can help you identify promising lead compounds and optimize them.

        2. Lead Optimization: We can assist you in optimizing lead compounds to create highly potent and selective drugs.

        3. Fragment Library Design: We can design custom fragment libraries to meet your specific needs and requirements.

    • Q: What are the software available for FBDD?
      • A: The following softwares are available for FBDD:

        1. Schrödinger: This is a suite of software tools for drug discovery, including molecular modeling, molecular dynamics, and virtual screening.

        2. OpenEye: OpenEye is used for molecular modeling and cheminformatics.

        3. Cresset: Cresset is used for ligand- and structure-based design.

    • Q: What are the features of FBDD service?
      • A: Our FBDD service offers a range of solutions to help you achieve your drug discovery goals quickly and efficiently. Some of the key features of our service include:

        1. Customized solutions: We provide customized solutions based on your unique needs, whether you need help with which stage of FBDD or other aspects of drug development.

        2. High precision and accuracy: Our FBDD services use the latest algorithms and software to ensure high precision and accuracy, resulting in more successful drug candidates.

    • Q: What are the evaluation metrics?
      • A: The metrics for evaluating the performance of generative models can be broadly classified into 3 types depending on the object of evaluation as follows:

        Metrics that evaluate the entire molecular set, whose main goal is to evaluate the difference between the generated set G and the existing set E. This includes metrics such as the average Tanimoto similarity coefficient algorithm SNN between two molecular sets.
        Metrics for evaluating individual molecules within a molecular set. The more widely used metrics include the synthesisability score (SA Score) and the quantitative drugability prediction (QED) coefficient
        Benchmark reviews (benchmark) suites for evaluating generative models.
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