Scientists at UC San Diego have developed a machine learning algorithm to simulate the time-consuming chemistry involved in the earliest phases of drug discovery, which could significantly streamline the process and open doors for never-before-seen treatments.
May 6 2024University of California - San Diego Identifying candidate drugs for further optimization typically involves thousands of individual experiments, but the new artificial intelligence platform could potentially give the same results in a fraction of the time. The researchers used the new tool, described in Nature Communications, to synthesize 32 new drug candidates for cancer.
Trey Ideker, senior author, professor in the Department of Medicine at UC San Diego School of Medicine and adjunct professor of bioengineering and computer science at the UC San Diego Jacobs School of Engineering The researchers trained POLYGON on a database of over a million known bioactive molecules containing detailed information about their chemical properties and known interactions with protein targets. By learning from patterns found in the database, POLYGON is able to generate original chemical formulas for new candidate drugs that are likely to have certain properties, such as the ability to inhibit specific proteins.
The researchers found that the drugs they synthesized had significant activity against MEK1 and mTOR, but had few off-target reactions with other proteins. This suggests that one or more of the drugs identified by POLYGON could be able to target both proteins as a cancer treatment, providing a list of choices for fine-tuning by human chemists.
Drugs Artificial Intelligence Drug Discovery Machine Learning Medicine Precision Medicine Technology
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