We have recently seen rapid progress in our understanding of the basic biology of aging. New scientific advances have provided proof of principle in mice that some drugs and genetic interventions can increase lifespan and healthspan, and treat multiple age-related diseases.


Newer high-throughput technologies such as metabolomics, transcriptomics, and methylomics have made it possible to measure aging directly in human populations. BioAge analyzes human aging cohorts that have samples and health records collected over several decades. In partnership with biobanks, we measure omics readouts and create unique datasets of genetic and biochemical data to identify factors that enhance longevity.


A number of molecular pathways and processes have been shown to significantly impact longevity. These include cellular senescence, genome stability, protein homeostasis, mitochondrial function and metabolism. We have identified targets that play roles in these areas, and we are actively building drug discovery programs around them. Each of these programs are focused on specific clinical indications.

Machine learning

BIOAGE takes a data-driven systems biology approach to find the molecular pathways that drive aging. By applying tools from machine learning and artificial intelligence to integrate genomic, proteomic and metabolomic information from human studies, we have identified pathways and factors that can be modulated to impact aging and age-related disease.