The article discusses the complexities of drug discovery, highlighting that only around 500 treatments have been developed for 7,000 rare diseases over the past century. Artificial intelligence (AI) could potentially revolutionize this field by improving the efficiency of drug design. An AI system can integrate molecular structures and a patient’s biological context to enhance drug-target interactions. However, the effectiveness of AI in drug development hinges on the quality of available data, which often suffers from inconsistencies and biases, potentially challenging the anticipated transformation in drug discovery.
Drug discovery is extraordinarily difficult. In 100 years or so of contemporary medicine, we've found treatments for only around 500 of the roughly 7,000 rare diseases.
AI should be able to bring together the 3D geometry and atomic structure of a potential drug-like molecule and build a picture of how it fits into its target protein.
The key to developing systems that are capable of boosting the drug-discovery process is lots of good data. Researchers have a solid foundation on which to build.
Although the scale of the available data might indicate that the AI transformation of drug development is surely just a matter of time, this will not necessarily be the case.
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