Charliste Hampton was an inaugural research intern at CSET during the summer of 2024 and is a senior at Spelman College in Atlanta, Georgia. 

How Does Computational Biology Work and What Are the Benefits?

Computational biology uses computers to understand biological mechanisms and systems with stored data. It can allow scientists to recreate, model, and predict various biological systems, from large organ interactions to smaller systems like simple cell function. Computational biology has already facilitated major scientific discoveries, and its benefits are quite significant. For example, humans are composed of tens of thousands of genes, and for years, scientists could only base scientific innovations and new drugs on snippets of human DNA. The Human Genome Project used computational biology to overcome this challenge by mapping many DNA fragments into a full human genome for the first time, helping researchers to see the correlations between genes and diseases and use that knowledge to create therapies. Virtual screening is another computational biological technique that sorts through databases of drug molecules and virtually tests thousands of them to find compatible drug candidates. This approach limits the pool of compounds that a researcher needs to individually test in a laboratory. Techniques like these have already proven themselves to be valuable assets to the research community and invaluable tools for improving human health. These techniques aid scientific innovation and medicine by creating treatment plans for blood diseases, neurological disorders, and other illnesses.

Integration of AI in Computational Biology

Artificial intelligence has the potential to improve the speed and efficiency of computational biology, which could lead to benefits for scientific innovation, human health and medicine, as well as the economy. AI can assist scientists by sorting and identifying patterns in biological data, simulating existing biological mechanisms, and predicting or generating new testable biological structures. Major U.S. policy recognizes these benefits too, including the Biden administration’s 2022 Executive Order on Advancing Biotechnology and Biomanufacturing, which states that “biotechnology harnesses the power of biology to create new services and products, which provide opportunities to grow the United States economy and workforce and improve the quality of our lives and the environment.”

Why is AI Useful for Computational Biology?

AI can be a useful addition to computational biology and future research innovations. For example, computational biologists used AI to address pressing problems like antimicrobial resistance, which contributes to over 4.9 million deaths per year. AI models performed virtual screening through the Broad Institute’s Drug Repurposing Hub, a database with over 6,000 compounds, and found that the drug Halicin, created years ago for diabetes, has the capacity to kill resistant bacteria. Furthermore, generative AI systems like AlphaFold can model potential protein structures using existing databases of known protein structures. This benefits drug design because it enables scientists to explore what compounds will be most compatible when creating drug therapies and treatment plans. Exploring the benefits of AI can create and contribute to a more cost efficient and less time-consuming drug development process, saving lives and improving patient health.

Research Trends in AI + Computational Biology

Our research team used data from CSET’s Emerging Technology Observatory to evaluate the current state of AI’s use for computational biology. This area of research is growing, and will likely continue to do so in the coming years. 

The Research Almanac is a user-friendly ETO tool that shows global trends in emerging technology research topics that are associated with AI. It identifies AI-related scholarly publications within a range of topic areas, and summarizes relevant metrics like the number of articles published per year, the institutions and countries that are publishing them, and associated patents. This blog post references the “AI + computational biology” page of the Research Almanac.

Computational Biology + AI Research is Growing

Figure 1: Global AI + Computational Biology Research Over Time

Source: ETO Research Almanac. For more, see: https://almanac.eto.tech/topics/ai-applications-computational-biology/.

• There are over 14,000 published AI + computational biology articles from 2017-2022. 
• While AI + computational biology articles only make up around 1% of all AI articles in the Research Almanac, the topic grew by 85%, suggesting that it may continue to grow globally and be an area of interest.
• Other biology topics in the Research Almanac are also growing in terms of their publishing presence. AI + Neuroscience grew 170% from 2017-2022 while AI + Pharmacology  grew 291% and AI + Genetics grew 167%. This suggests that artificial intelligence is a fast-growing tool that is beneficial to a diverse range of biological studies and research.  

For further details on how the Research Almanac classifies articles, see the documentation.

AI + Computational Biology Research is Global

Figure 2: AI + Computational Biology Research Over Time

Source: ETO Research Almanac. For more, see: https://almanac.eto.tech/topics/ai-applications-computational-biology/.

• For AI + computational biology research publications between 2017-2021, 28% have authors from China, 21% have authors from the United States, and 20% have authors from European countries. The United Kingdom is the third leading country with 7% of published articles. This suggests that the United States and China can have a significant impact on the field as leading contributors to AI + computational biology publications.
• There is a range of global interest in research with AI + computational biology  which suggests that this may be an area that is beneficial to advancing scientific innovations or the quality of life of individuals.
• Harvard University, Stanford University, and the Chinese Academy of Sciences produced the most top-cited articles.¹ This suggests that these institutions have the capacity to set the direction of AI + computational biology research.

Fast-Growing AI + Computational Biology Research is Varied

Our research team looked at a different ETO tool, the Map of Science, which collects and organizes the research literature, revealing key trends, hotspots, and concepts in global science and technology. 

The Map of Science includes 76 AI + computational biology clusters, of which 15 have predicted extreme growth. Below are some of the particularly interesting fast-growing clusters.

• Cluster 28594 Key Themes: drug combinations, synergistic drug, drug synergy, cancer cell lines, and combination therapy(694 articles)
  • Important Research Articles
    • Advance Computational Prediction of Cancer Drug Combinations
      • AI and computational biology can help find more efficient cancer treatments. Computational biological databases are filled with multiple chemical compounds and molecules. AI models can sort through these molecules, create simulations, and analyze the data in these servers to be able to predict new combinations of drugs in order to combat constantly evolving tumor cells. 
• Cluster 77967 Key Themes: vaccine candidates, reverse vaccinology, vaccine development, potential vaccine, and machine learning(172 articles)
  • Important Research Articles
    • AI being used for Covid-19 Drug Discovery and Vaccine Development
      • Databases can also be filled with data from  microorganisms, and so the COVID-19 pandemic sent the computational biology research community into a race to find a vaccine. Researchers used the predictive power of AI to build biological models and simulations that predict which chemical compounds are best suited as a defense against viruses and other microorganisms like bacteria.
• Cluster 45389 Key Themes: microbiome data, human microbiome studies, data analysis, compositional data analysis, and gut microbiome (776 articles)
  • Important Research Articles:
    • Machine Learning Methods for Microbiome Studies
      • The human microbiome is filled with millions of different microorganisms, alongside numerous genes that contribute to our health and well-being. Computational biologists are using AI in order to sort and predict what types of drugs, treatment plans, and therapies would be most beneficial for each individual’s specific microbiome.

While the sector of AI + computational biology is a small percentage of AI research, its high growth rate suggests that it has the potential to be an advantageous biotechnology.

U.S. policymakers should be aware that advances in AI are not just  valuable to the tech industry but also to scientific innovations and even medicine more broadly. Just like our research community, machine learning programs are already benefiting our healthcare system by assisting physicians in predicting more efficient healthcare treatments and improving the quality of patient care. Policymakers should consider the positive impacts of AI and computational biology, especially as it continues to make significant impacts in research and medicine improving overall quality of life.

About the Author

I am a senior and a biology major at Spelman College in Atlanta, Georgia. Growing up in the rural south, I witnessed health disparities and environmental injustices, and these experiences inspired my passion for advocacy and research. As an intern for Georgetown University’s Center for Security and Emerging Technology in Washington, D.C., I have been able to explore artificial intelligence and its potential to benefit biotechnology and medical discoveries. Being from a community with limited access to healthcare served as an important personal and eye-opening experience for me. I believe that investing in technology will likely have a positive impact on communities like mine. Precision medicine is just one of the areas of particular interest to me, as it approaches personalized treatment plans that do not just take into account genetic differences but the environmental and demographic differences of each individual. Advocacy is an important aspect of my life and it fuels my passion for scientific innovation, health equity, and research as a means to improve the quality of life for so many people.

1. Top-cited research = the 10% of articles in each year with the most citations. Note that some articles lack information about author nationality, and articles without English-language titles or abstracts are omitted. For further details, see the Research Almanac documentation.