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March 23, 2021

Moving Forward to Artificial Intelligence Drug Discovery

We believe AI and Machine Learning offer the hope of a streamlined and automated approach across various stages in drug discovery. The AI-enabled AIMCADD® platform helps us to identify new targets and treatments easier, and better.

 

Thanks to an explosion in knowledge about the molecular mechanisms behind different diseases, more promising targets for the design of new drugs are being identified than ever before. However, the complexity and the unknown of the E3 system are still challenges and good opportunities to scientists. AnHorn Medicines wants to build a systematic selection tool to break the lengthy discovery process. With this in mind, we spent 3 months of effort to build AI-enabled AIMCADD®, and we successfully generate new candidates of novel E3 ligase for further study.

 

Leveraging the bioinformatics at the public domain, AI-enabled AIMCADD® captures and displays the interacting protein in the E3 domain, the associate E3 ligase and the protein expressions at different levels. The integrated results can help scientists to identify novel E3 ligase with better association with disease-causing proteins.

 

Next step, we use linguistic machine learning to screen the most relevant biomedical literatures. The scientist will be able to narrow down to a limit number of highly relevant articles and focus on those articles for further study. From there we successfully find the novel E3 candidates (2-3 options) for further study.

 

A snapshot of AI-enabled AIMCADD®
AI-Enabled-AIMCADD-by-AnHorn-Medicines

 

The roadmap toward Artificial Intelligence Drug Discovery is an ongoing journey of AnHorn Medicines. Our next step is to explore the possibility to automate the drug design process. We envision the AI-molecular modeling helps improve new drug planning and eliminates avenues that will not scale viably.
 

 

AnHorn Medicines vision ourselves a shed of light in biotechnology, we are turning innovation to new therapeutics, and creating new opportunities to target difficult-to-treat diseases through our technology.

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