机器学习辅助蛋白质工程的最佳实践
Fabio Herrera-Rocha1, David Medina-Ortiz1,2, Fabian Mauz1
1Leibniz-Institute of Plant Biochemistry, Department of Bioorganic Chemistry, Weinberg 3, D-06120 Halle, Germany.
Journal of chemical information and modeling
|November 17, 2025
概括
这一观点概述了在蛋白质工程中开发可靠机器学习 (ML) 模型的指导方针. 它强调软件工程的最佳实践,以提高ML研究的透明度和可信度.
科学领域:
- 计算生物学 计算生物学
- 生物化学 生物化学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 越来越多地成为蛋白质工程工作流程的组成部分.
- 开发有效,可靠和可重复的ML模型对于推进该领域至关重要.
研究的目的:
- 介绍蛋白质工程中强大的ML模型开发的基本元素和准则.
- 促进基于ML的蛋白质工程研究的透明度和可信度.
主要方法:
- 讨论用于ML开发和评估的软件工程最佳实践.
- 强调监督学习方法.
- 该指南涵盖了ML开发的整个生命周期,从数据采集到部署.
主要成果:
- 一套全面的指导方针,用于蛋白质工程中的ML开发.
- 实施这些指导方针的实际资源.
- 为科学期刊和编辑推实施良好实践.
结论:
- 通过这些准则将提高ML透明度和蛋白质工程的可信度.
- 标准化最佳实践将促进对ML的明智应用,以应对现实世界的蛋白质工程挑战.
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