机器学习在抗体发现和开发中的最佳实践.
Leonard Wossnig1, Norbert Furtmann2, Andrew Buchanan3
1LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
Drug discovery today
|May 18, 2024
概括
机器学习 (ML) 加快了治疗性抗体的设计和开发. 标准化ML方法和数据对于抗体发现和应用的可靠进展至关重要.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- 在过去的40年里,治疗性抗体的发现取得了重大进展.
- 机器学习 (ML) 提供了加速这一过程的机会,降低了成本和实验代.
- 目前在抗体设计和开发 (D&D) 中的ML应用因数据和评估方法的多样性而面临挑战.
研究的目的:
- 批判性地审查ML引导的治疗抗体D&D.当前的做法.
- 确定阻碍ML在该领域有效应用的常见陷.
- 提出方法开发和评估的指导方针,以提高ML的实用性和采用.
主要方法:
- 对基于ML的抗体D&D.现有文献和实践的审查.
- 分析与数据多样性和评估指标相关的挑战.
- 为机器学习过程的每个阶段制定最佳实践建议.
主要成果:
- ML有可能显著提高治疗抗体D&D的效率.
- 缺乏标准化的数据集和评估方法阻碍了进步和可比性.
- 在D&D管道中识别ML实施中的特定陷.
结论:
- 建立明确的标准和准则对于ML在治疗抗体D&D中的广泛采用和推进至关重要.
- 实施推的最佳实践将提高可复制性,加快现场进展.
- 标准化的方法是释放ML在新型抗体疗法中的全部潜力的关键.
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