This article introduces “Adaptive CVgen,” a novel reinforcement learning-based sampling method designed to capture complex, dynamic behaviors in molecular systems, particularly in protein folding and chemical synthesis. Traditional experimental techniques, like cryo-electron microscopy, struggle to capture these transient dynamics, and while methods like molecular dynamics simulations exist, they fall short on long timescales. Adaptive CVgen addresses this by using high-dimensional collective variables (CVs) that provide detailed views of system evolution, capturing subtle structural changes. Reinforcement learning enhances its predictive accuracy by iteratively refining CVs based on historical data, enabling extensive conformational space exploration. This technique has shown success in diverse applications, including tracking protein folding dynamics and modeling fullerene synthesis, with potential applications across drug development, materials science, and catalysis.
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