Drug Apocalypse of the Future: Artificial Intelligence Discovers New Antibiotics, Disrupting Traditional Innovations

At present, the use of artificial intelligence to participate in the discovery and design of new drugs has become a hot track for new drug research and development. Once, the Massachusetts Institute of Technology used artificial intelligence technology to successfully discover a new antibiotic - Halicin (Halicin). The antibiotic exhibited the strongest antimicrobial effect in history, and through the autonomous learning and analysis of the artificial intelligence model, it successfully screened for molecules with excellent inhibition of bacterial growth. Through deep learning of 2,000 known spirit molecules, the AI model not only discovered new antibiotic characteristics, but also accurately screened out a highly effective antibiotic in an ultra-large product library. This article takes Halisin as an example to analyze this. 

Fig.1 Molecular structure of Halicin

▲Compared with other antimicrobial drugs, what are the characteristics of the new drugs discovered by AI?

The antibiotic halisin has a powerful bactericidal effect on bacteria that have developed resistance on the market and does not induce new resistance. Compared with traditional verification methods, the screening speed using AI models is extremely fast, which greatly reduces the cost. Interestingly, Halisin exhibited features previously ununderstood by human scientists, a discovery that sheds new light on the field of antibiotic research. However, the nature of this feature is still unknown, and no clear answer can be found even in the training of AI models. This research results show that the application of artificial intelligence in the field of drug discovery has gone beyond the limitations of traditional human methods.

Artificial intelligence has led to a more efficient, cost-effective, and innovative drug discovery process in drug discovery. This transcendence is primarily reflected in the speed of development, cost-effectiveness, and a new understanding of drug properties. For example, AI models can screen potential drug candidates more quickly and efficiently, significantly shortening the time frame for drug discovery compared to traditional experimental validation methods. Leveraging AI models for drug screening significantly reduces the cost of R&D and is more cost-effective than traditional methods. AI models have demonstrated the ability to reveal previously ununderstood drug features that may be difficult to detect in traditional research methods, thus providing more innovative directions for the development of new drugs.

▲Why is it named Halicin, a strange name?

Halicin's original name actually has only one code, called SU-3327, which is actually just an experimental drug, or a prototype of the drug. It was initially studied for the treatment of diabetes, but due to poor test results, the application development of the compound has long been discontinued and is only used as an experimental drug. Later, artificial intelligence (AI) models found that halisin has antibiotic properties against a variety of bacteria. And from this it was officially named. Its name, "Halicin", is a reference to Hal, a fictional artificial intelligence system in 2001: A Space Odyssey.

"2001: A Space Odyssey" is a classic science fiction movie, and it plays a pivotal role in the history of science fiction films and is regarded as one of the milestones of science fiction films. Known for its innovative visuals, music, and storytelling, the film sets the bar high for future sci-fi films. It presents a universe full of mystery and wonder, allowing the audience to think deeply about the future of humanity and the development of science and technology. It has become a part of the global sci-fi culture and has had a profound impact on future sci-fi movies and TV shows. 

▲What is its specific effect and prospects?

When halisin was first discovered, researchers used computer deep learning methods to determine that halisin may be a broad-spectrum antibiotic. This possibility was confirmed by in vitro cell culture testing and in vivo experiments in mice, showing activity against a number of drug-resistant strains, including Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis. Its mechanism of action involves the sequestration of iron within bacterial cells, thereby interfering with its ability to regulate pH balance on cell membranes. Preliminary research suggests that halisin may kill bacteria by disrupting their ability to maintain an electrochemical gradient on cell membranes. This mechanism of action is different from that of most antibiotics and may make it difficult for bacteria to develop resistance. Overall, halisin has demonstrated potential as an antibiotic, especially for some bacteria that have developed resistance to conventional drugs. 

However, this new drug was first reported in 2019, but there is still no news of a new drug or any updates, which may have encountered difficulties in subsequent new drug research and development. There are two reasons for the initial suspicion: First, the unsatisfactory clinical trial results in the development of new drugs may be due to the failure of the initial trials to fully demonstrate the efficacy or safety of the drug. In this case, the R&D team may need to re-evaluate the suitability of the drug and take measures to modify or optimize it. On the other hand, safety and toxicity issues are also an important consideration, and new drugs need to pass rigorous toxicity and safety evaluations before they are launched. If adverse safety issues or toxicities are identified during the development process, additional studies and modifications may be required to ensure the safety of the drug for patients.

▲ Summary

In summary, machines have great potential in drug discovery, especially when dealing with complex information. Through deep learning, machines can quickly find patterns and accelerate new drug discovery. Second, collaboration is key. A super-team of computer, biological and pharmaceutical experts is the key to successfully using AI to develop new drugs. This collaboration overcomes traditional R&D challenges and improves efficiency. The success of the machine also tells us that data is crucial. Big data and deep learning allow us to have a more accurate understanding of drug effects and design drugs in a more targeted manner. Overall, AI makes the development of new drugs more efficient and brings new possibilities to the medical field. With the addition of more powerful artificial intelligence, the future of robotic drug discovery is about to become a reality.

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