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Developing Drug Targets

Target selection is the starting point of drug discovery project. Innovative drug development should be based on innovative drug targets. AI can learn in depth to uncover the relationship between drugs and diseases, find effective targets and shorten the period of target discovery, so as to speed up the drug development process. At CD ComputaBio's AI platform, through analyzing massive literature, patents and clinical results with data collection and mining system, our scientists apply machine learning models to gather mountains of data to assess whether targets are "promising".

Our Services

Highlights

  • Integrate hundreds of biomedical databases and acquires massive information
  • Identify novel targets, phenotype and clinical correlates
  • Efficient tool to access, visualize and infer knowledge through a virtual knowledge network
  • Discover new targets for disease treatment and biomarkers for diagnosis.

Key Capabilities

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Data-driven new knowledge

Learn in depth to find the relationship between drugs and diseases, find effective targets and shorten the period of target discovery, so as to speed up the drug development process.

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New therapeutic target discovery

The combination of drug targets with synergistic effect was identified by using AI driving platform and automatic design ability.

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Efficiently parallelized and cloud scalable

Analyze a large number of samples of patients and normal people. Find new targets for disease treatment and biomarkers for disease diagnosis.

Artificial Intelligence in Developing Drug Targets

Successful identification of a new drug target requires detailed molecular target evaluation, including experimental study of pharmacodynamic properties based on disease hypothesis and theoretical evaluation of molecular drug, as well as initial idea of biomarkers related to potential target. Machine learning algorithm can be applied to traditional single target drug discovery projects with the deep knowledge of coding.

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