Originally published January 7, 2017
Here is an excerpt:
Another important biological hurdle that AI can help people surmount is complexity. Experimental science progresses by holding steady one variable at a time, an approach that is not always easy when dealing with networks of genes, proteins or other molecules. AI can handle this more easily than human beings.
At BERG Health, the firm’s AI system starts by analysing tissue samples, genomics and other clinical data relevant to a particular disease. It then tries to model from this information the network of protein interactions that underlie that disease. At that point human researchers intervene to test the model’s predictions in a real biological system. One of the potential drugs BERG Health has discovered this way—for topical squamous-cell carcinoma, a form of skin cancer—passed early trials for safety and efficacy, and now awaits full-scale testing. The company says it has others in development.
For all the grand aspirations of the AI folk, though, there are reasons for caution. Dr Mead warns: “I don’t think we are in a state to model even a single cell. The model we have is incomplete.” Actually, that incompleteness applies even to models of single proteins, meaning that science is not yet good at predicting whether a particular modification will make a molecule intended to interact with a given protein a better drug or not. Most known protein structures have been worked out from crystallised versions of the molecule, held tight by networks of chemical bonds. In reality, proteins are flexible, but that is much harder to deal with.
The article is here.