Drug repositioning is an effective approach to accelerate the development of new drugs while reducing the cost of numerous efforts. The challenge is to identify the right existing compounds to investigate for a given disease. AI is now able to integrate and use many different types of data much more effectively than before.
Relying on MedAI's AI-powered platform, with the help of natural language processing and deep learning abilities, our experts team can extract knowledge and put forward new hypotheses from the scattered and disordered mass information. In addition to in-silico modeling and target docking using algorithmical solutions beyond medicinal chemistry, data mining of side effects and drug interactions will be a way to find valuable clues for APIs formulated into marketed drugs.
Few data sources are specifically tuned to the needs of drug repurposers. Analysis tools are not yet sufficiently advanced to assist without extensive training in pathway and systems medicine.
Advanced AI technologies can look for connections they have not been specifically trained to identify will revolutionize drug repositioning within the next few years, taking us beyond mere expert systems.
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