Isomorphic Labs internally prioritizes drug discovery programs targeting oncology, immunology, and inflammation

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In animal model experiments, mice fed a high-fructose diet exhibited increased metastatic cancer progression compared to mice fed a glucose-based diet

Fructose alters mitochondrial signaling and reduces membrane cholesterol in cancer cells, diminishing cellular adhesion

In ovarian cancer research, cancer cells surviving chemotherapy entered cellular senescence and ceased replication

Chemotherapy-surviving senescent ovarian cancer cells secrete fructose into their surrounding tissue microenvironment

More than 300,000 men in the United States receive a prostate cancer diagnosis each year

A study conducted in Europe over more than two decades found 13 percent fewer deaths among men aged 55 to 69 who underwent regular antigen screening for prostate cancer

The American Cancer Society recommends that most men receive routine screening for prostate cancer beginning at age 50

Fructose secreted by senescent cancer cells promotes the detachment and metastatic dissemination of neighboring cancer cells

Traditional drug development relies on empirical trial-and-error screening processes rather than predictive computational modeling

Isomorphic Labs partners with biopharmaceutical companies to apply its artificial intelligence drug design engine to partner drug targets

As of November 2026, the average development cost of a new drug has risen to approximately 1.5 billion dollars

Most candidate drugs fail during human clinical trials rather than during preclinical in-vitro research

Isomorphic Labs utilizes automated laboratories to generate experimental data to power computational models

Developing a new drug traditionally takes approximately 15 years from initial discovery to regulatory approval

Automated wet laboratories and robotic platforms remain constrained by physical reaction rates and cell culturing times

Cancer tumors frequently evolve resistance to single-target therapies through rapid genetic mutations

Patients facing late-stage terminal cancers typically possess a higher tolerance for treatment risk than patients treated for chronic low-level inflammation

Traditional pharmaceutical research typically treats each drug program as a bespoke project, limiting transferable learnings to subsequent programs

Despite an expansion in biomedical literature and drug modalities, the annual number of new drug approvals has remained roughly flat

Data repositories from historical pharmaceutical research often reflect biased sampling that limits machine learning model generalization