Drug discovery began as a serendipitous, trial-and-error effort. Then serendipity was industrialized with the introduction of synthetic chemistry and high-throughput screening which enabled us to screen millions of compounds to find potential drug candidates.

We are now creating a new era of predictable discovery led by exponential growth in biochemical data over the last decade and far more powerful computational tools such as scaled graph deep learning models and LLMs. We can now decode the complex language of chemistry computationally and understand the structure-function relationships in any molecule – bioactivity, toxicity, bioavailability, solubility, etc. - to efficiently predict its potential to become a drug. And when we apply these powerful tools to aggregate and then survey the landscape of human-qualified chemistry at industrial scale, we can predictably and hyper-efficiently find the best possible Anthromolecule keys to unlock any biology pathway door in less than 6 months.

Montai is aggregating the world's largest, fully-annotated knowledge graph of Anthromolecule chemistry - privileged molecules that have co-evolved with humans for thousands of years through exposure to food, herbs, and traditional medicines. There are 100,000+ known today, offering vastly greater chemical diversity compared to the 2,700 approved drugs - with millions more to discover.

Anthromolecule chemistry is already a proven source of chronically-dosed, FDA-approved drugs for many diseases such as Parkinson's, asthma, and heart disease, but has yet to be systematically mined for drug development.