Compound Library Design

At CD ComputaBio, we specialize in cutting-edge AI-based compound library design services to revolutionize drug discovery and development processes. Our advanced computational methods, combined with artificial intelligence technologies, offer unparalleled precision and efficiency in creating compound libraries tailored to meet the unique needs of our clients. With a commitment to innovation and excellence, we strive to be at the forefront of pharmaceutical research by delivering customized solutions that drive success in the discovery of novel therapeutics.

What is Custom Compound Library Design?

Compound libraries play a crucial role in the drug discovery pipeline by providing a diverse collection of molecules for screening against biological targets. The quality and diversity of a compound library directly impact the success rate of identifying lead compounds with desired pharmacological properties. Custom compound library design involves the creation of a specialized collection of compounds tailored to a specific research or screening project. This process typically involves selecting compounds based on their structural or functional properties that are relevant to the target of interest.

Fig 1. Compound designFig 1. Compound design

Our Services

  • De Novo Design

Fig 2. Compound library design

Our de novo design service involves the generation of novel compound structures that meet specified criteria such as target activity, ADMET properties, and chemical diversity. Through AI-guided molecular design, we can rapidly explore vast chemical space to propose structurally diverse molecules with the potential for drug development.

  • Diversity Analysis

Fig 3. Compound library design

We conduct comprehensive diversity analysis of compound libraries to ensure they cover a wide range of chemical space, maximizing the chances of identifying hits with varied biological activities. Our AI algorithms analyze structural descriptors and similarity metrics to assess the diversity of the library and guide further optimization strategies.

  • Retrosynthetic Analysis

Fig 4. Compound library design

Using retrosynthetic analysis combined with machine learning techniques, we predict synthetic routes for lead compounds in the library. This predictive approach allows for efficient exploration of feasible synthesis pathways, enabling the design of synthesizable and scalable molecules for experimental validation.

  • Property Prediction

Fig 5. Compound library design

Our AI models accurately predict key molecular properties such as solubility, bioavailability, and toxicity, aiding in the selection of compounds with optimal drug-like characteristics. By integrating property prediction into our design process, we enhance the quality and safety profiles of the generated compound libraries.

Compound Library Design Computational Methodologies

Structure-based virtual screening

Using crystal structures or homology models of the target protein to identify compounds that are predicted to bind effectively.

Ligand-based virtual screening

Screening compounds based on their similarity to known active compounds or pharmacophores.

Similarity/substructure searching

Identifying compounds with structural similarities to known active compounds in order to expand the chemical diversity of the library.

Statistical/categorical model generation

Developing predictive models based on the properties of known active compounds to prioritize new compounds for screening

Our Advantages

Our AI-driven compound library design services leverage machine learning algorithms, predictive modeling, and data analytics to generate virtual compound libraries with desirable properties. By harnessing the power of artificial intelligence, we can predict molecular interactions, assess drug-likeness, and prioritize compounds with the highest potential for further development. This systematic approach enables us to streamline the identification of promising lead compounds while minimizing experimental costs and time. If you are interested in our services or have any questions, please feel free to contact us.


  • Merk D, Friedrich L, Grisoni F, et al. De novo design of bioactive small molecules by artificial intelligence[J]. Molecular informatics, 2018, 37(1-2): 1700153.
  • Hao Y, Romano J D, Moore J H. Knowledge-guided deep learning models of drug toxicity improve interpretation[J]. Patterns, 2022, 3(9).


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