Google DeepMind is an artificial intelligence (AI) company, known for its important developments in the fields of AI, deep learning and large language models (LLMs; introducing various tools such as Gemini, Imagen, Veo and more, which many of you probably use). Two of its senior scientists, Demis Hassabis and John Jumper, shared the 2024 Nobel Prize in Chemistry with David Baker, a biochemist (and a computational biologist). It may sound surprising that scientists in an AI company receive the Nobel Prize in Chemistry for the prediction of a protein’s 3D structure from its amino acid sequence, one of the holy grails of biology. However, for those who follow the advancements in biology and computer science in the last decades, this is not so surprising. The award was given for their role in the development of AlphaFold, a tool that uses advanced deep learning techniques applied to biological big data to predict protein 3D structure. AlphaFold has paved the way for many other tools (even introducing Apple to the field, with its SimpleFold tool) that together are already having a big impact on biological and clinical research, including novel drug discovery and design.

The Nobel Prize in Chemistry 2024

 Source: The Nobel Prize website.

AlphaFold is an excellent example of the growing synergy between biology and computer science, but is only a small part of the story. Biological, and specifically genetic, research has made tremendous progress over the last decades. Of course, this progress is based on important biological discoveries, such as the helical structure of the DNA, how DNA is copied and translated, together with the understanding of key cellular processes. However, part of the progress was due to the advancement in related (and what also seemed at that time as unrelated) technologies. Next generation sequencing techniques allow scientists to sequence genomes at a fraction of the cost of what it was a decade or two ago, therefore allowing much more sequencing data to be analyzed. In fact, the reduction in sequencing cost since the days of the Human Genome Project is much faster than  Moore’s Law, which reflects the improvements in the microprocessors industry (which lies at the heart of the computer’s CPU). 

Reduction in sequencing costs in the last two decades suppresses Moore’s Law.
Source: National Human Genome Research Institute (NHGRI)

These improvements make genetic data the real “big data” (see here), and require a suitable way to digitally store and process huge amounts of genomic data. Luckily, these needs were and are being met with similar improvements in the computer science industry and community over the last decades. Thus, genomic data can be stored efficiently in the cloud, and processed with advanced tools to extract genomic insights (e.g., detect genetic variants using various tools such as GATK, and Google’s DeepVariant), later serving as important platforms and databases for innovative research. For example, the UK Biobank stores the genetic information of 500K individuals to a total of 27.5 petabytes data, together with extensive clinical and phenotypic data, and allows researchers to gain important insights with clinical implications. As genomic data (and “omics” data in general) accumulates, more advanced tools are applied to gain insights from the data (e.g., Google DeepMind’s AlphaGenome for the annotation of genomic sequences). Following the growing role of computational approaches for biological research, more and more multidisciplinary teams that combine both computational and biological knowledge are found in research (both in academia and in industry), as well as people who themselves combine these areas (i.e., computational biologists and bioinformaticians). Beyond basic research (and just to list a few examples), computational biology teams and tools allow for the understanding of the basis of diseases, introducing novel drug targets (and also allow for the development of relevant drugs), design suitable gene therapies, and more. In the agricultural world, these tools allow for deciphering the genomes of diverse crops, discovering the genetic basis of desired traits that enhance yield and resistance, transforming modern breeding. At NRGene, we apply these same principles using proprietary advanced computational tools to analyze large-scale genomic datasets, often combined with phenotypic data, to deepen our understanding of key biological features that drive progress for our breeding teams and clients.

But the synergy between biology and computer science works in the other direction too: computer science gains from biological insights as well, both for software / algorithms and hardware / architecture. In terms of algorithms, the giant leap in AI applications in recent years is heavily based on deep learning artificial neural network approaches that try to mimic the neuronal structure of the brain. Similarly, genetic algorithms try to find better solutions to complex problems similar to the way natural selection acts on biological systems. Biology can also promote computer hardware / architecture. DNA is a way to efficiently and accurately store and copy genetic information. Within each human cell there are two copies of the human genome (a total of 6 billion nucleotides) efficiently packed. The “alphabet” of the DNA is composed of four “letters” (the “A”, “G”, “C” and “T” nucleotides) and these can be used to store information similar to the way information is coded by the binary code (0/1) in computers. Therefore, DNA can be used to store and retrieve digital information. For example, scientists recently demonstrated how huge amounts of data can storeed in a DNA “cassette”. 

Schematic of a DNA cassette. Source: Li et al., Science Advances 2025

As biological data continues to expand and related technologies advance, we can anticipate a growing number of significant insights and breakthroughs driven by computational tools and resources. Therefore, it will be no surprise to witness future biological or clinical advancements – likely additional Nobel Prizes – that emerge from computational analysis.