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".
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.
The combination of drug targets with synergistic effect was identified by using AI driving platform and automatic design ability.
Analyze a large number of samples of patients and normal people. Find new targets for disease treatment and biomarkers for disease diagnosis.
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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