深度批量主动学习用于蛋白质结构建模
Zexin Xue1, Michael Bailey1, Abhinav Gupta2
1R&D Data & Computational Science, Sanofi, Cambridge, Massachusetts, USA.
概括
DEWDROP是一种新的主动学习方法,可以战略性地选择数据,以改善对像VHH抗体等代表性不足的蛋白质的分子结构预测,有效地提高模型性能.
科学领域:
- 结构生物学和生物信息学
- 计算机化药物发现和开发.
- 在分子建模中的机器学习应用.
背景情况:
- 准确的分子结构预测对于制药研究和了解蛋白质功能至关重要.
- 当前的深度学习模型虽然先进,但在预测VHH抗体等代表性不足的分子结构方面存在局限性.
- 实验性结构确定是耗时和昂贵的,使得大规模的数据收集用于模型培训是不切实际的.
研究的目的:
- 开发一个战略数据选择方法,DEWDROP,通过代微调来提高分子结构预测模型的性能.
- 通过优化新实验数据的策划,解决现有培训数据集中分子领域代表性不足的挑战.
- 通过最大限度地提高所选结构的信息内容,以减少代和成本,实现卓越的模型性能.
主要方法:
- 提出了DEWDROP,一种使用蒙特卡洛脱落的积极学习选择方法,用于生成最佳数据选择的预测集.
- 采用了基于粗粒度分子表示的结构化预测模型Equifold,独立于多个序列对齐.
- 在VHH抗体 (SAbDab-nano) 和*Mycobacterium leprae*蛋白 (AlphaFold Protein Database) 上进行了追溯代微调实验和批量选择分析.
主要成果:
- 通过优化批量选择,DEWDROP显著提高了模型训练效率,在代微调中表现优于基线方法.
- 该方法成功地识别和选择了具有高信息内容的结构信息化数据,这对于提高预测准确度至关重要.
- 证明了DEWDROP在VHH抗体之外的不同分子领域的有效性和更广泛的适用性.
结论:
- DEWDROP为结构生物学中的战略数据选择提供了一个模型不可知的方法,特别有利于代表性不足的分子家族.
- 积极学习策略提高了改善分子结构预测深度学习模型的效率和成本效益.
- 这种方法通过最大限度地提高新获得的实验结构数据的价值,促进了优越的模型性能.
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