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7, you together to reduce the chances that a drug-resistant variant will emerge. (This is done now with HIV treatments: Three antivirals are combined, limiting the spread of resistant viruses.) These libraries need to be accessible to all researchers so they can use different approaches to find out what should be added to the library. Use artificial intelligence and other software to develop antivirals and antibodies faster. Several companies are doing great work in this field. Essentially, you would build a 3D model of the pathogen that you want to target--it could even be one we've never seen before--and also models of various drugs that you think might work against it. The computer would quickly run these models against each other and then tell you which drugs look promising. It can figure out how to improve drugs or even design them from scratch. Speed up clinical trials. A few efforts, such as the RECOVERY trial in the U.K., set up ready-to-go protocols and built up infrastructure that made it much easier to get started once COVID hit. We should build on those models, improving the ability to do trials around the world so we can learn what works even when a new disease is present in just a few countries. Regulators need to agree in advance on how people will be enrolled and on the software tools that will enable enrollment globally, as soon as the disease strikes. And by connecting diagnostic reports into the trial system, we can automatically suggest to doctors that their patients should join a large-scale trial. Diagnostics