Anthropic launches Claude Science: artificial intelligence aims to rewrite the rules of biomedical research in Kendall Square
The development of a new drug remains one of the most costly and uncertain challenges in contemporary science. The process can take many years and requires huge financial investments, with the vast majority of tested candidates never reaching patients. This reality is even more pronounced in the case of biological drugs, therapies derived from engineered proteins rather than traditional synthetic chemical compounds.
26 October 2026 · 5 min
The development of a new drug remains one of the most costly and uncertain challenges in contemporary science. The process can take many years and requires huge financial investments, with the vast majority of tested candidates never reaching patients. This reality is even more pronounced in the case of biological drugs, therapies derived from engineered proteins rather than traditional synthetic chemical compounds.
According to MIT Technology Review AI, the complexity of these biological therapies, increasingly used in the treatment of autoimmune diseases, cancer, and other serious conditions, makes the traditional discovery process extremely slow. Each candidate protein must be designed, synthesized, and tested in the laboratory, and failures are the rule, not the exception.
How algorithms are changing the rules of the game
In this context, an increasing number of research teams are turning to artificial intelligence tools capable of simulating and predicting the structure and behavior of proteins before they are actually tested in the laboratory. Instead of physically testing thousands of variants, scientists can use computational models to drastically narrow down the number of viable candidates, thereby saving considerable time and resources.
These systems learn from vast databases containing information about known protein structures, molecular interactions, and previous experimental results. Based on these patterns, algorithms can suggest modifications to amino acid sequences that could enhance the efficacy of a therapy or reduce unwanted side effects.
Real impact on the pharmaceutical industry
Pharmaceutical companies and biomedical research institutes are heavily investing in such platforms, considering them essential for maintaining competitiveness in a market where the speed of development can make the difference between success and commercial failure. Reducing the time required to identify a promising candidate theoretically means faster treatments for patients and lower costs for healthcare systems.
However, experts cited by the American source caution that artificial intelligence does not completely eliminate the need for rigorous clinical testing. Computational models provide valuable predictions, but the final validation of a drug's safety and efficacy remains a process that involves human studies conducted over several years.
As these technologies mature, the scientific community appears optimistic about the ability of AI to fundamentally transform the way future biological drugs are designed.