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Fragment-Based Drug Design

Welcome to CD ComputaBio, your premier partner in the field of fragment-based drug design. Our company specializes in providing cutting-edge solutions for drug discovery and development, leveraging the power of computational tools and expertise to accelerate the drug design process. With a focus on innovation and excellence, we strive to deliver tailor-made solutions that meet the unique needs of our clients in the pharmaceutical and biotechnology industries.

Our Services

Fragment based screening

Fragment Screening

Utilizing state-of-the-art computational algorithms and molecular modeling techniques, we conduct virtual fragment screening to identify small molecule fragments that can serve as building blocks for drug design.

Drug design of fragment hit expansion

Fragment Hit Expansion

Once hit fragments are identified, we leverage our expertise to expand and optimize these hits into lead-like compounds through fragment elaboration and optimization strategies.

Ligand-based design service

Ligand-Based Design

Using ligand-based approaches such as pharmacophore modeling and QSAR analysis, we conduct ligand-based design to develop novel compounds with desired pharmacological properties.

Analysis Methods

Fragment-Based Approaches

Our fragment-based drug design strategies involve fragment linking, growing, and merging to generate novel lead compounds with enhanced binding affinity and selectivity.

Molecular Dynamics Simulations

We perform molecular dynamics simulations to study the dynamic behavior of protein-ligand complexes, providing valuable insights into binding mechanisms and energetics.

Quantum Mechanics Calculations

For accurate prediction of chemical reactivity and binding affinities, we apply quantum mechanics calculations to analyze the electronic structure and energy landscapes of drug molecules.

Highlights of AI-Based Drug Design

Many machine learning methods, such as QSAR methods, SVMs or random forest methods, are established during the drug discovery process. New algorithms based on neural networks, such as deep neural networks, provide further improvements for attribute prediction, which have been revealed in many benchmark studies comparing deep learning and classic machine learning.

Multiple routes to expanding fragment to more drug-like molecule with improved binding affinity

Samples Requirements & Result Delivery

Samples Requirements
  • Meet at least 4 of the Lipinski Five Rules;
  • The structure cannot be too complicated (SA Score≤4.5)
  • The molecule has a good molecular docking score.
Delivery Mode The simulation results provide you with the raw data and analysis results of molecular dynamics.

Our Advantages

Industry Expertise

Our team comprises seasoned experts with extensive experience in computational drug design, structural biology, and pharmaceutical research, ensuring the highest quality of service delivery.

Cost-Effective Solutions

We offer competitive pricing structures and flexible service packages that provide excellent value for money, allowing you to maximize your research budget and resources.

Rapid Turnaround

With our streamlined workflows and efficient processes, we offer rapid turnaround times without compromising on the quality and accuracy of our results.

CD ComputaBio is your trusted partner for fragment-based drug design services, offering a comprehensive suite of solutions to accelerate your drug discovery efforts. With our advanced methodologies, industry expertise, and commitment to excellence, we are dedicated to supporting your success in developing novel and effective therapeutics. If you are interested in our services or have any questions, please feel free to contact us.

References:

  • Scott D E, Coyne A G, Hudson S A, et al. Fragment-based approaches in drug discovery and chemical biology[J]. Biochemistry, 2012, 51(25): 4990-5003.
  • de Souza Neto L R, Moreira-Filho J T, Neves B J, et al. In silico strategies to support fragment-to-lead optimization in drug discovery[J]. Frontiers in chemistry, 2020, 8: 93.

Services

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