Machine learning helps predict drugs' favorite subcellular haunts

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Machine learning helps predict drugs' favorite subcellular haunts
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Most drugs are small molecules that bind firmly to a specific target—some molecule in human cells that is involved in a disease—in order to work. For example, a cancer drug's target might be a molecule that is abundant inside of cancer cells. The drug should hypothetically travel freely throughout the cell until it comes to its target and then lock onto it, leading to a therapeutic action.

However, small molecule drugs do not travel in such an unrestricted manner; instead, they tend to concentrate in specific regions of the cell. This is because each drug is capable of interacting with many more molecules than its target.

This work shows that interactions between condensates and small molecules help to determine where in the cell a small molecule will end up and what it will interact with, which may be relevant to understanding manyIf a large percentage of a small molecule drug, for instance, ends up in a condensate that does not contain the drug's target, then much higher doses of the drug may be required for it to work, increasing the likelihood of toxicity and unintended side effects.

Young lab postdoc Henry Kilgore and graduate student Kalon Overholt, co-first authors on the new paper, wondered what they would learn if they systematically tested whether and how different drugs concentrate in different condensates. First, they tested a large swathe of drugs to confirm that it is a common occurrence for drugs to concentrate in specific compartments rather than dispersing freely throughout the whole cell: they found that it is.

The model found that the molecules that favored each type of condensate tended to have shared chemical features, and were more like each other than like molecules that favored other condensate types. It identified a number of features that seem to affect where molecules end up. For example, transcriptional condensates tended to attract small molecules containing electron-rich aromatic rings .

In the meantime, the researchers hope that this work demonstrates the importance of re-thinking how cells are organized, and considering where molecules concentrate based on their chemical features.

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