Big-Data Analysis Points Toward New Drug Discovery Method

Publié le 19 juillet 2017, par Thérèse Hameau

A research team led by scientists at UC San Francisco has developed a computational method to systematically probe massive amounts of open-access data to discover new ways to use drugs, including some that have already been approved for other uses.

The method enables scientists to bypass the usual experiments in biological specimens and to instead do computational analyses, using open-access data to match FDA-approved drugs and other existing compounds to the molecular fingerprints of diseases like cancer. The specificity of the links between these drugs and the diseases they are predicted to be able to treat holds the potential to target drugs in ways that minimize side effects, overcome resistance and reveal more clearly how both the drugs and the diseases are working.

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