Life science research has always been a crucial pathway for humans to explore the mysteries of themselves and nature. In this process, we often encounter various challenges and obstacles. One of the most persistent problems biologists have been trying to solve is determining the three-dimensional structures of large biomolecules (such as proteins, DNA, RNA) and their interactions.
Traditional experimental methods, such as X-ray crystallography and nuclear magnetic resonance, can provide accurate structural information but often take years and are costly. This has greatly limited our progress in studying life molecules. However, with the rapid development of artificial intelligence technology, this situation is being rewritten.
In 2020, Google DeepMind released AlphaFold 2, which won the 14th Critical Assessment of Protein Structure Prediction (CASP14) challenge with a score far exceeding the runner-up, achieving a breakthrough in solving the protein folding problem. In May 2024, DeepMind once again brought the latest evolution of AlphaFold – AlphaFold 3, pushing the application of artificial intelligence in the field of biology to a new height.
Core Capabilities of AlphaFold 3
AlphaFold 3 is a powerful AI model that can predict not only protein structures but also the structures of various biomolecules including DNA, RNA, and small molecules (such as drugs), as well as their interactions. This makes AlphaFold 3 a universal model covering all life molecules.
Compared to previous generations of AlphaFold, AlphaFold 3 has made huge improvements in prediction accuracy and applicable range. In the PoseBusters benchmark test for predicting how drug-like small molecules bind to proteins, AlphaFold 3 improved prediction accuracy by 50% over existing best methods, becoming the first AI system to surpass physical methods in this task.
How does AlphaFold 3 work? It uses a deep learning architecture called “Evoformer,” which learns the correspondence between amino acid sequences and protein structures by training on a large number of known protein structures. When predicting the structure of a new protein, AlphaFold 3 first processes the amino acid sequence, then uses a network similar to a diffusion model, starting from a cloud of atoms and gradually optimizing and converging to generate an accurate molecular structure.
In addition to structure prediction, AlphaFold 3 can also predict interactions between different molecules. This is crucial for understanding biological functions because molecules in living systems do not exist in isolation; they interact, bind, and regulate each other to carry out complex life activities together.
Overall, AlphaFold 3 provides biologists with an unprecedentedly powerful tool. With this system, researchers only need to provide sequence information of molecules to obtain structural and interaction data in minutes that would have taken years of experiments to determine in the past. This will greatly accelerate the progress of biological research, allowing us to uncover the mysteries of life faster and more efficiently.
Applications of AlphaFold 3 in Biomedicine
AlphaFold 3 has broad application prospects in the biomedical field. Drug development is one of the most important directions. Developing a new drug requires a large investment of time and money, much of which is used for screening and optimizing lead compounds. Traditional drug screening methods, such as high-throughput screening, can find active molecules in vast compound libraries but often have problems such as high false positive rates and poor specificity.
Using AlphaFold 3, we can simulate the binding of small molecules to target proteins on computers, quickly predict their affinity and binding modes, and greatly narrow down the range of candidate drugs. At the same time, by optimizing the binding mode of drug molecules to targets, we can improve the selectivity and effectiveness of drugs and reduce side effects. This will significantly accelerate the process of new drug development, shorten the time for new drugs to market, and benefit a wide range of patients.
Isomorphic Labs, a Google subsidiary focused on AI-driven drug discovery, is applying AlphaFold 3 to multiple drug development projects. By integrating AlphaFold 3 with other AI models developed independently by the company, Isomorphic Labs is exploring new drug design methods, challenging previously intractable drug targets, and paving the way for developing breakthrough therapies.
In addition to drug development, AlphaFold 3 also has great application potential in disease diagnosis and precision medicine. The occurrence of many diseases is closely related to structural abnormalities and functional disorders of proteins.
AlphaFold 3 Driving Fundamental Biological Research
The impact of AlphaFold 3 is not limited to the biomedical field; it also shines in basic biological research. Understanding the structure and function of proteins has always been a core issue in biology. Proteins are involved in almost all life activities, such as enzyme catalysis, signal transduction, immune response, cell skeleton construction, etc. And the function of proteins largely depends on their unique three-dimensional structure. Therefore, analyzing protein structures is the basis for understanding their biological functions.
However, despite the completion of the Human Genome Project more than 20 years ago, our understanding of human protein structures is still very limited. This is mainly because experimentally determining protein structures is very difficult and progress is slow. The emergence of AlphaFold is expected to completely change this situation.
In July 2021, DeepMind announced the release of structural predictions for all human proteins, covering about 20,000 known proteins in the human body. In July 2022, they further expanded the AlphaFold database to more than 200 million protein structures, covering almost all known protein sequences in the scientific community. This milestone achievement marks the entry of structural biology into a new era.
With a massive amount of high-confidence protein structures, biologists can systematically compare and analyze these structures to mine the biological laws contained within. For example, through structural alignment, we can discover the evolutionary relationships of homologous proteins between different species; by looking for structural domains and motifs, we can infer the functional categories and regulatory sites of proteins; by analyzing structural dynamics, we can understand the mechanism by which proteins perform their functions.
In addition, the study of DNA and RNA molecular structures is also of great significance for understanding important biological issues such as gene expression regulation, RNA interference, and nucleic acid aptamers. AlphaFold 3 provides powerful tool support for research in these fields.
Conclusion
The combination of AI technology and biological research represented by AlphaFold 3 is opening a new era in life sciences. It not only provides new ideas and tools for tackling major scientific challenges such as protein folding but also fundamentally changes the paradigm of biological research. In this era of big data and artificial intelligence, biological research is developing from descriptive observation to quantitative, systematic, and predictable directions.
Of course, the development and application of any new technology require a process. Although the prediction accuracy of AlphaFold is already very high, it is still some distance from perfection. In the future, how to further improve accuracy, especially for predicting difficult-to-fold proteins, membrane proteins, and protein complexes, will be an important direction. In addition, AlphaFold only predicts static structures, while proteins change dynamically in vivo. Therefore, developing protein dynamics simulation methods based on AlphaFold will also be a topic worth attention.
Nevertheless, the path of integration between biology and artificial intelligence represented by AlphaFold has been opened and will irreversibly lead life sciences to new heights. We have reason to believe that in the near future, with the help of AI systems, humans will make exciting breakthroughs in revealing the mysteries of life, overcoming disease troubles, delaying aging, exploring extraterrestrial life, and other aspects. Let’s look forward to this upcoming new era of AI + life sciences together.
