From Evo 1 to Evo 2: How NVIDIA is Redefining Genomic Research and AI-Driven Biological Innovations

Imagine a world where we could predict the behavior of life by only analyzing a series of letters. This is not a science fiction or a magical world, but a real world where scientists have been striving to achieve this goal for years. These sequences, consisting of four nucleotides (A, T, C and G), contain the fundamental instructions for life on earth, from the smallest microbe to the greatest mammal. Decoding these sequences has the potential to unlock complex biological processes, to transform fields such as the personalized medicine and sustainability of the environment.

Despite this enormous potential, the decoding of even the simplest microbial taken is a very complex task. These taken consist of millions of DNA base bogs that regulate the interactions between DNA, RNA and proteins – the three important elements in the central dogma of molecular biology. This complexity exists on several levels, from individual molecules to whole, creating a huge field of genetic information that developed over a period of billions of years.

Traditional computational tools have difficulty cope with the complexity of biological sequences. But with the rise of generative AI, it is now possible to scale trillions of sequences and understand complex relationships about sequences of tokens. Building on this progress, researchers from the ARC Institute, Stanford University and Nvidia have worked on building an AI system that can understand biological sequences such as large language models, understand the human text. Now they have made a pioneering development by creating a model that records both the multimodal nature of the central dogma and the complexity of evolution. This innovation can lead to predicting and designing new biological sequences, from individual molecules to whole. In this article we will investigate how this technology works, the potential applications, the challenges with which it is confronted and the future of genomic modeling.

EVO 1: A groundbreaking model in genomic modeling

This research received attention at the end of 2024 when Nvidia and his employees introduced EVO 1A groundbreaking model for analyzing and generating biological sequences about DNA, RNA and proteins. Trained on 2.7 million prokaryotic and paagged, a total of 300 billion nucleotide dockens, the model focused on integrating the central dogma of molecular biology, modeling the stream of genetic information from DNA to RNA to proteins. The strippedhyena architecture, a hybrid model with conventional filters and ports, treated efficiently long contexts of up to 131,072 tokens. With this design, EVO could link 1 small sequence changes to broader system-wide and at organism level effects, which bridged the gap between molecular biology and evolutionary genomics.

EVO 1 was the first step in computational modeling of biological evolution. It successfully predicted molecular interactions and genetic variations by analyzing evolutionary patterns in genetic sequences. However, as scientists wanted to apply it to more complex eukaryotic, the limitations of the model became clear. EVO 1 struggled with resolution with one nucleotide over long DNA sequences and was computational expensive for larger taken. These challenges led to the need for a more advanced model that is able to integrate organic data on multiple scales.

EVO 2: A fundamental model for genomic modeling

Building on the lessons of EVO-1, his researchers have been launched EVO 2 In February 2025, promoting the field of biological sequence modeling. Trained On a stunning 9.3 trillion DNA base bogs, the model has learned to understand and predict the functional consequences of genetic variation in all domains of life, including bacteria, archaea, plants, fungi and animals. With more than 40 billion parameters, the EVO-2 model can handle an unprecedented length of a maximum of 1 million base pairs, something that previous models, including EVO-1, could not manage.

What distinguishes EVO 2 from its predecessors is the ability to model not only the DNA sequences, but also the interactions between DNA, RNA and proteins – the entire central dogma of molecular biology. This allows EVO 2 to accurately predict the impact of genetic mutations, from the smallest nucleotide changes to larger structural variations, in ways that were previously impossible.

An important feature of EVO 2 is the strong zero-shot prediction capacity with which it can predict the functional effects of mutations without requiring task-specific refinement. For example, classifies clinically significant BRCA1 variants, a crucial factor in breast cancer research, by only analyzing DNA sequencies.

Potential applications in biomolecular sciences

The possibilities of EVO 2 open new frontiers in Genomics, Molecular Biology and Biotechnology. Some of the most promising applications are:

• Healthcare and drug discovery: EVO 2 can predict which gene variants are associated with specific diseases that help in the development of targeted therapies. For example, in tests With variants of breast cancer-associated gene BRCA1, Evo 2 reached more than 90% accuracy when predicting which mutations are good-hearted versus potential pathogen. Such insights can speed up the development of new drugs and personalized treatments. ​
• Synthetic biology and genetic manipulation: The ability of EVO 2 to generate whole takes new roads opens when designing synthetic organisms with desired properties. Researchers can use EVO 2 to develop genes with specific functions, to promote the development of biofuels, environmentally friendly chemicals and new therapy.
• Agricultural biotechnology: It can be used to design genetically modified crops with improved properties such as drought resistance or resilience of vermin, which contributes to global food security and sustainability of agriculture.
• Environmental science: EVO 2 can be applied to the design of biofuels or engineer proteins that break down environmentally pollutants such as oil or plastic, which contribute to sustainability efforts.

Challenges and future directions

Despite its impressive possibilities, EVO 2 stands for challenges. An important obstacle is the computational complexity that is involved in training and implementing the model. With a context window of 1 million base pairs and 40 billion parameters, EVO 2 requires significant calculation sources to function effectively. This makes it difficult for smaller research teams to fully use the potential without access to high-performance computing infrastructure.

In addition, although EVO 2 is shining in predicting genetic mutation effects, there is still much to learn how to use new biological systems completely redesign. Generating realistic biological sequences is only the first step; The real challenge lies in understanding how to use this power to create functional, sustainable organic systems.

Accessibility and democratization of AI in Genomics

It is one of the most exciting aspects of EVO 2 open-source availability. To democratize access to advanced genomic modeling tools, Nvidia has made model parameters, training code and data sets public. This open-access approach enables researchers from all over the world to explore and expand the possibilities of EVO 2, which accelerates innovation in the scientific community.

The Bottom Line

EVO 2 is an important progress in genomic modeling, in which AI is used to decode the complex genetic language language. The ability to model DNA sequencies and their interactions with RNA and proteins opens new opportunities in health care, discovering medicines, synthetic biology and environmental sciences. EVO 2 can predict genetic mutations and design new biological sequences, which offers transforming potential for personalized medicine and sustainable solutions. However, the computational complexity is challenges, especially for smaller research teams. By making EVO 2 open-source, Nvidia enables researchers worldwide to explore and expand its possibilities, to stimulate innovation in genomics and biotechnology. As the technology continues to evolve, it is the potential to reform the future of biological sciences and environmentalism.