Sinclair's Team Completes 160 Years of Molecular Screening in Two Months Using AI
David Sinclair's team used AI to screen 200 potential "super molecules" from one trillion molecules in two months, aiming to find a single compound that can simultaneously exhibit three to four activities and rejuvenate cells.
The team built a virtual funnel on computers, utilizing DeepMind's protein structure data for docking simulations, compressing a task that traditionally takes thousands of years into just 60 days; next week, these 200 molecules will enter human cell testing, with AI assessing whether they can reverse aging from a 92-year-old state to a 20-year-old state.
Computational power replacing manual screening significantly accelerates drug discovery, leading to a rapid emergence of longevity research candidates, benefiting small laboratories while putting pressure on traditional pharmaceutical companies' R&D cycles.
Source: Public Information
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Sinclair has long focused on epigenetic reprogramming and anti-aging, previously advancing with gene therapy and cocktail approaches, now shifting to single multi-active small molecules to simplify administration.
Virtual screening relies on protein structure prediction and large-scale docking, motivated by the aim to break through the traditional pharmaceutical industry's single-target limitations; similar pathways can be seen in other AI-driven de novo drug design cases.
Compared to the trillion-level screening that was previously unattainable by manual methods five years ago, we are now in a phase where AI is shifting biological exploration from experimental bottlenecks to computational priorities.
This is essentially a technological replacement: computational power compresses thousands of years of experimental time into months, with the mechanism being that structure prediction and molecular docking transform high-throughput screening from physical to virtual.
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