Longevity InTime: Autonomous AI Institute. Anti-Aging Digital Health Immortality Transhumanist AI Channel

AI Drug Search Patrick Maloone proposed evaluating AI-powered drug searches by selecting candidates for laboratory testing on August 20. In his post, Maloone described his AI-powered drug search model, where scientists guide multiple specialized programs to narrow down thousands of protein or molecule variants to a short list for laboratory testing. The model can propose a large number of plausible protein or molecule variants, but the laboratory can only synthesize and test a small fraction of them. Maloone formulated this gap as: "100,000 plausible molecules are only useful when you can choose 20 that are worth testing". He drew inspiration from the recent Claude campaign on miniprotein design, where an agent selected a protein segment, design programs, and candidate lists, and two laboratories then tested whether they bound to the target protein. In Maloone's scenario, a scientist assigns programs to individual stages: literature search, modeling, design, result analysis, and experiment planning. To prioritize candidates, one needs to select a program for a specific question, distribute computations, implement control checks, preserve data provenance, and set evaluation rules. Then, one needs to troubleshoot failures, as described in Nature Aging, July 2026. 🔗 Read original →