The model learns from vast datasets of genomic sequences, allowing it to identify biologically meaningful patterns that can lead to new insights in biology.

Our model, EVO, is designed to predict the next base in a DNA sequence, which can determine health outcomes, such as the presence of sickle cell anemia mutations.

The model can also predict the next amino acid residue, which is crucial for determining the functionality of enzymes in biological processes.

We are training models inspired by evolutionary forces to connect biological sequences directly to their functions, leveraging the effects of natural selection through DNA mutations.

There's a significant opportunity in generating in vivo data, which is currently limited, to better understand drug interactions in real biological systems, and we're seeing breakthroughs in data-generating technology in biology and chemistry that will significantly impact how we model these fields.

Generative models are essential for navigating the vast molecular space, allowing us to explore potential designs that could lead to effective therapeutics.

In medical diagnostics, models can demonstrate impressive reasoning capabilities, mapping symptoms to correct medical cases effectively.

The integration of genetic information with health biomarkers will lead to more accurate predictions of health outcomes, moving beyond traditional methods.

EVO2 is a revolutionary biological foundation model that interprets and generates genomic sequences, identifying patterns in genetic code and predicting the effects of mutations that can impact health.