Google DeepMind Is Trying To Solve A Huge Problem In Healthcare: Antimicrobial Resistance

Antimicrobial resistance (AMR), also known generally as “drug resistance,” is a common yet concerning aspect of healthcare. Given how dependent society has become on antimicrobial medications, bacteria, viruses, and fungi have slowly evolved to outsmart human-therapeutics to live longer in the body, meaning that patients will remain resistant to common treatments and be sicker for longer periods of time. Eventually, if innovation of new drugs and therapeutics does not keep up with the rate of microbial evolution, there will be a massive emergence of “superbugs” which even the strongest drugs cannot combat.

Google DeepMind is working to make breakthroughs in this arena, especially in the structural biology and generative modeling spaces. Using advanced machine learning and platforms such as Google Antigravity, an agentic development platform, the goal is to eventually make the development and discovery of novel antimicrobials easy and feasible. The platform enables scientists and biologists to evaluate structures, develop compounds, and understand drug targets more easily. For example, researchers can use it to study multitudes of previously uncharacterized proteins in the UniProt database using ProtNLM. UniProt is a comprehensive database of protein sequences that scientists globally use as the gold standard repository to understand biological functions, structure, cellular location, disease, and taxonomy. ProtNLM is Google’s own deep learning model built specifically to understand the grammar and foundations of amino acid sequences. Together, the model can help label previously uncharacterized UniProt entries to enable new targets and therapeutics.

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