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Ligand-based Drug Design


When the 3D information of receptor is absent or unavailable, ligand based drug design is a useful tool to discover novel drugs employing the knowledge of molecules that bind to the biological target of interest. A knowledge-based approach for lead identification is a very efficient way of developing novel chemical matter using ligand-based computational tools. Alfa Chemistry is committed to offering multiple computational techniques such as 3D quantitative structure activity relationships (3D QSAR) and pharmacophore modeling, which enable to provide predictive models suitable for lead identification and optimization.

Schematic representation of the ligand-based drug design.Figure 1. Schematic representation of the ligand-based drug design. (Fukunishi, Y.; Nakamura, H. 2012)

Application of Ligand-based Drug Design

  • Structure evaluation of new compounds
  • Improve poor chemical structure
  • Predict the bioactivity and ADMET properties of new compounds

Our Services

  • Quantitative structure activity relationship (QSAR)

QSAR is a method that correlates molecular structure with properties like in vitro or in vivo biological activity. We use the structure of a molecule including its geometric, steric and electronic properties which contain the features responsible for its physical, chemical, and biological activities to model physicochemical properties.

1. We perform 3D-QSAR study using CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Index Analysis) techniques, and our methods can provide important information:

1) Explore the important physicochemical properties

2) Identify essential structural and physicochemical sites required for binding events

2. Multidimensional QSAR: We have established 3D-QSAR, 4D-QSAR and 5D-QSAR to account for local changes in the binding site.

3. Receptor-dependent 3D/4D-QSAR: We use this free energy force field 3D/4D-QSAR to create a ligand-receptor force field QSAR model that describes all thermodynamic contributions for binding.

4. Linear regression: At Alfa Chemistry, multivariable linear regression analysis (MLR), principal component analysis (PCA), or partial least square analysis (PLS) are available for the molecular interaction field algorithm CoMFA and CoMSIA.

5. Nonlinear models using machine learning algorithms: We mainly apply artificial neural networks (ANNs), support vector machine (SVM) and decision tree method to perform QSAR-based drug discovery.

  • Pharmacophore modeling strategies

As a very useful tool for hit identification, pharmacophore based virtual screening has been applied when the three-dimensional (3D) structure of the target is unknown. We conduct a series of computational steps including drug target selection, database preparation, pharmacophore model generation and 3D screening to perform pharmacophore mapping. Our groups use it to perform rapid screening of millions of compounds for identification of potential candidates.

We have developed a comprehensive process involving the following steps:

1) Determine the features required for a particular biological activity

2) Determine the molecular conformation required such as the bioactive conformation

3) Develop a superposition or alignment rule for the series of compounds

  • Similarity searches

Alfa Chemistry applies fingerprint methods to search data bases for compounds similar in structure to a lead query, providing an extended collection of compounds that can be tested for improved activity over the lead.

1. We perform 2D similarity searches of data bases employing various chemotype information.

2. We can conduct similarity ensemble approach (SEA) to compare drug targets based on the similarity of their ligands and predict whether a ligand and target will interact.

3. Our scientists are capable of converting a set of query molecules into a topological model (MTree) based on chemically reasonable matching of corresponding functional groups.

Features of Ligand-based Drug Design

  • Molecular descriptors/features

Our experts apply different methods for describing features of small molecules using computational algorithms:

1. Functional groups.

2. Prediction of psychochemical properties such as electronegativity and partial charge, polarizability, octanol/water partition coefficient.

3. Converting properties into descriptors: binary molecular fingerprints, 2D description of molecular constitutionm, 3D description of molecular configuration and conformation.

  • Molecular fingerprint

Molecular fingerprint relies entirely on chemical structure and omit compound known biologic activity and they can be used in the following application:

1. Enrichment of lead compound population.

2. Increase molecular diversity of test compounds.

Our Advantages

  • We are capable of selecting optimal descriptors/features for optimal performance of ligand-based drug development.
  • Our groups have accumulated rich experience in pharmacophore mapping with multiple pharmacophore feature extraction methods and pharmacophore algorithms.
  • Alfa Chemistry utilizes quantum mechanics-based structural optimization and molecular dynamics-based conformation analysis to predict the stable conformation and flexibility of the compound, helping to better understand the binding mode of the compound and the target.

Our ligand-based drug design services remarkably reduce the cost, promote further experiments, and accelerate the process of drug design for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!


  • Fukunishi, Y.; Nakamura, H. Integration of Ligand-Based Drug Screening with Structure-Based Drug Screening by Combining Maximum Volume Overlapping Score with Ligand Docking. Pharmaceuticals. 2012, 5(12): 1332-1345.

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