Researchers developed a new way to identify how diseases might cause or influence one another by analyzing scientific literature and validating the results using real-world patient data. They searched through PubMed articles for phrases suggesting that one disease leads to another, then standardized those disease names using ICD-10-CM medical codes to keep the data consistent.

To test whether these suggested relationships were credible, the team used a combination of five validation methods. They looked at how strongly diseases were statistically linked in the UK Biobank dataset, whether the timing of diagnoses followed the expected pattern (with the “cause” usually diagnosed before the “effect”), and how frequently the relationships appeared in the literature. They also tested how dependent the diseases were on each other and asked GPT-4, a powerful AI language model, to assess the plausibility of each connection. All of this information was combined into a confidence score for each relationship.

Chatbots like ChatGPT have made it easy for users to access complex information through simple questions. The same principle is now being applied to one of biology’s most complex challenges: protein design. Traditionally, creating custom proteins required deep technical expertise and reliance on naturally evolved templates. But scientists are now building AI models that can generate novel proteins from plain English prompts, much like asking ChatGPT for a summary or essay.

Enter Pinal, a new AI designed to act as a conversational protein engineer. Developed by an international team of researchers, Pinal allows scientists to describe the desired type, function, or structure of a protein in natural language. In response, the AI generates candidate proteins that can be tested in living cells. In one demonstration, Pinal successfully designed enzymes that broke down alcohol, some even functioning at high temperatures.