用自适应算法和智能调度，让蛋白质设计更高效可靠。
Adaptive Protein Design Protocols and Middleware
- 根据设计进展动态调整计算资源分配
- 实现蛋白设计质量稳定与吞吐量提升
- 适合需要大规模高精度蛋白设计的研究者
计算蛋白质设计正因人工智能/机器学习而变革。然而，即使对中等大小的蛋白质，其可能的序列与结构组合也极其庞大，生成与预测结构收敛需大量计算资源进行采样。集成式大规模蛋白质结构机器学习系统（IMPRESS）提供了方法与先进计算架构，将AI与高性能计算任务结合，使在设计过程中可实时评估设计效果，以及生成数据和训练模型所用的模拟与模型性能。本文介绍了IMPRESS系统，并展示了自适应蛋白质设计协议及其支撑计算基础设施的开发与实现。该系统通过动态资源分配与异步工作负载执行，提升了蛋白质设计的一致性与整体吞吐量。
原文摘要 · Abstract (English)
Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial computational resources for sampling. The Integrated Machine-learning for Protein Structures at Scale (IMPRESS) offers methods and advanced computing systems for coupling AI to high-performance computing tasks, enabling the ability to evaluate the effectiveness of protein designs as they are developed, as well as the models and simulations used to generate data and train models. This paper introduces IMPRESS and demonstrates the development and implementation of an adaptive protein design protocol and its supporting computing infrastructure. This leads to increased consistency in the quality of protein design and enhanced throughput of protein design due to dynamic resource allocation and asynchronous workload execution.
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