Introduction

The 2024 Nobel Prize in Chemistry marked a significant shift in computational biology, recognizing the development of AlphaFold (AF) technology. This advancement changed structural research by turning the slow, labor-intensive process of

determining the three‑dimensional (3D) structure of proteins into one capable of producing near-instantaneous predictions (Desai et al., 2024; Fang et al., 2025). AF was created by Google DeepMind along with Isomorphic Labs, and it is an artificial intelligence system that can predict the 3D structures and interactions of a range of biological molecules, including proteins, DNA, RNA, and ligands (Fang et al., 2025; Zheng et al., 2025). By modeling these molecular relationships with high accuracy, AF accelerates scientific research, allowing scientists to quickly generate and evaluate hypotheses about cellular interactions, genetic mutations, and drug-protein binding (Figure 1).

Through the lens of Science, Technology, and Society (STS), AF should not be viewed as a neutral or self-sufficient system (Lin, 2024; Valohai, n.d.). Its impact is shaped by the expertise of the scientists who use it and the sociotechnical structures that control its access and implementation. While AF represents a major breakthrough in addressing the challenge of protein structure prediction, its full potential will depend on addressing inequalities in access and aligning its development with open science principles. Ultimately, AF is one example of how artificial intelligence is more powerful when it is used to enhance human knowledge and judgement rather than replace it altogether.

Connection to STS

AlphaFold is an example of a technology whose “knowledge” is shaped by its design and training data (Fang et al., 2025). AF doesn’t have a deep physical understanding of molecular interactions, rather it demonstrates pattern recognition and “memorization” by using previously known interactions on which it was trained. This was shown by Zheng et al., who found that AF had a significant decline in predictive accuracy for protein structures released after its training ended. From an STS perspective, this emphasizes the importance of not treating AF as an independent problem solver, as it experiences “hallucinations” and conformational biases (Desai et al., 2024; Fang et al., 2025).

Unfortunately, the breakthrough with AF is limited by an infrastructure gap that introduces exclusion within the scientific community. The demands of AF, such as its reliance on high-performance GPU systems, serve as a barrier for underfunded institutions or those with limited resources (Valohai, n.d.). Additionally, because AF is trained on existing protein databases, it can take on biases in available data. This may include prioritizing well-characterized diseases while leaving rare or less-studied conditions underrepresented. This gap is worsened by tensions between private corporations and academic institutions. For example, Google DeepMind published AlphaFold3 (AF3) in Nature without releasing its source code (Lin, 2024). This resulted in criticism from more than 1,000 scientists who argued that these restrictions create a system of access that hinders global scientific progress. This highlights that AF is a tool whose impact is shaped by the sociotechnical systems governing its accessibility, data, and use.

Information on the Topic

AlphaFold Technology

AlphaFold was developed in 2018 and was ultimately awarded the Nobel Prize in Chemistry in 2024 for providing a largely accurate solution to the challenge of protein folding predictions (Fang et al., 2025). AF3 was built on earlier versions, such as AF2, which focused on predicting individual protein structures. AF3 expanded its ability by modeling interactions of DNA, RNA, ligands, proteins, and even drugs (Desai et al., 2024; Fang et al., 2025; Zheng et al., 2025). This Diffusion Transformer System begins with a diffuse cloud of atomic positions and progressively refines its arrangement through iterative design until a stable 3D model is reached, such as that shown in Figure 2 (Desai et al., 2024; Fang et al., 2025).

This approach differs from other protein-visualization techniques, as it is faster, producing predictions within seconds, and requires less specialized equipment and fewer financial resources to uncover a single structure.

Scientific Impact

The most significant contribution of AF3 is its ability to accelerate scientific discovery. It has predicted more than 200 million structures, which expands the availability of structural data on a large scale (Fang et al., 2025). In drug discovery, this ability has improved the identification of viable therapeutic targets by increasing the accuracy of protein side-chain positioning compared to traditional docking approaches (Zheng et al., 2025).

AF3 also supports insights into disease mechanisms and biological processes. For example, researchers can now investigate how specific genetic mutations alter protein structure and function in diseases like Duchenne muscular dystrophy and multiple forms of cancer (Desai et al., 2024; Fang et al., 2025). By allowing fast, large-scale hypothesis generation and testing, AF3 has shifted structural biology into a field driven by computational exploration.

Applications

The application of AF3 is important to both academic and industry research. In pharmaceutical and biotechnology settings, AF3 is integrated into drug development pipelines to increase the speed of early-stage research, which helps reduce costs by filtering out ineffective drug candidates before they reach clinical trials (Desai et al., 2024; Fang et al., 2025).

Another area of application is personalized medicine, where predictions can be combined with genomic data, such as protein quantitative trait loci (genetic markers), to design treatments tailored for individual patients (Desai et al., 2024; Gadde et al., 2024). Researchers have already applied AF3 to model more than 1,200 brain-related proteins, which have allowed them to identify therapeutic targets for neurodegenerative diseases like Alzheimer’s and Parkinson’s. This shows how the AF3 technology can support precise and targeted approaches to clinical application.

Technical Limitations

Despite its potential, AF3 has limitations in accuracy and interpretation. Importantly, its “hallucination” effect, which causes the model to predict structured regions in proteins that are truly disordered, can lead to incorrect conclusions if the results are not carefully evaluated by researchers (Desai et al., 2024; Fang et al., 2025). Evidence suggests that AF3 depends largely on recognizing patterns from its training data rather than developing a mechanistic understanding of the molecules’ behavior (Zheng et al., 2025).

Another limitation is its inability to capture the full dynamic nature of the molecules (Fang et al., 2025; Zheng et al., 2025). Proteins are not static; they constantly change conformation in response to other molecules and cellular events. AF3 generates static predictions and doesn’t account for those changes, which can be problematic for proteins that exist in multiple functional states. One such example is that AF3 tends to predict the active form of G protein-coupled receptors (GPCRs) regardless of whether the interacting ligand would induce activation or inhibition (Zheng et al., 2025). This bias is significant because GPCRs are among the most important drug targets in medicine, as they detect external signals, and mispredicting their activation state can lead researchers to draw incorrect conclusions about how potential therapies will behave in the body. These constraints of hallucination and conformational bias emphasize the importance of interpreting and analyzing AF’s outputs.

Conclusion

AlphaFold 3 challenges how scientific knowledge is produced, validated, and shared. As tools like AF3 become more available and embedded into average research work, they will allow scientists to focus more on interpretation and critical evaluation of computational outputs rather than spending decades to discover one lead for a project. Moving forward, however, the most important steps are not just to improve the model’s accuracy but to build a system that allows this technology to be used inclusively and responsibly. This means developing standards for validation, increasing transparency in model design, and expanding global access to computational infrastructure. It also requires continued communication between researchers, policymakers, and institutions to make sure that ethical consideration is held to just as high a standard as innovation. AlphaFold is a wonderful tool that can predict relationships between important molecules and reshape how data, tools, and human knowledge work together to shape the future of biomedical research.

References 

Articles

Desai, D., Kantliwala, S. V., Vybhavi, J., Ravi, R., Patel, H., & Patel, J. (2024). Review of AlphaFold 3: Transformative advances in drug design and therapeutics. Cureus, 16(7), e63646. https://doi.org/10.7759/cureus.63646

Fang, Z., Ran, H., Zhang, Y., Chen, C., Lin, P., Zhang, X., & Wu, M. (2025). AlphaFold 3: An unprecedented opportunity for fundamental research and drug development. Precision Clinical Medicine, 8(3), pbaf015. https://doi.org/10.1093/pcmedi/pbaf015

Zheng, H., Lin, H., Alade, A. A., Chen, J., Monroy, E. Y., Zhang, M., & Wang, J. (2025). AlphaFold3 in drug discovery: A comprehensive assessment of capabilities, limitations, and applications. bioRxiv. https://doi.org/10.1101/2025.04.07.647682

Lin, F. (2025, June 19). AlphaFold 3 angst: Limited accessibility stirs outcry from researchers. Genetic Engineering & Biotechnology News. https://www.genengnews.com/topics/artificial-intelligence/alphafold-3-angst-limited-accessibility-stirs-outcry-from-researchers/

Valohai. (n.d.). Why most AlphaFold pipelines fail at scale (and what to do instead). https://valohai.com/blog/why-most-alphafold-pipelines-fail/

Gadde, N., Dodamani, S., Altaf, R., & Kumar, S. (2024). Leveraging AlphaFold 3 for structural modeling of neurological disorder-associated proteins: A pathway to precision medicine. bioRxiv. https://doi.org/10.1101/2024.11.18.624211

Images

AI Use Acknowledgements

Copilot was used on 2/21/2026 to find resources for the chapter with the following prompt: “Could you please generate a list of 6 resources to support an open access textbook chapter for college students related to AlphaFold’s use in biomedical research? This list should include both books and articles.” I used 2 of the sources to gather information pertaining to my chapter’s topic.

I used ChatGPT on 3/19/2026 to help organize and improve the flow of my chapter. I used the following prompt: “Please bold any poorly written statements or anything that is redundant and suggest ways to improve them.” I used some of its suggestions to improve the flow but not others.

Lastly, I used Copilot on 3/25/2026 to proof-read and make final edits to my draft. I used the following prompt: “Please proof-read my chapter and highlight any places I should improve before submitting for feedback from my professor and peers.” I didn’t use any of its suggestions because I felt they complicated the chapter beyond what would be needed for the textbook.