低数据药物发现的深度学习:障碍和机遇
Derek van Tilborg1, Helena Brinkmann2, Emanuele Criscuolo3
1Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands; Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Princetonlaan 6, 3584 CB, Utrecht, the Netherlands. Electronic address: https://twitter.com/DerekvTilborg.
Current opinion in structural biology
|April 26, 2024
概括
深度学习在药物发现方面表现有前途,但在有限的数据上扎. 新的低数据学习方法正在出现,以克服这些挑战并推进制药研究.
科学领域:
- 计算化学和化学信息学
- 医学中的人工智能
- 药物的发现和开发.
背景情况:
- 深度学习 (DL) 在药物发现中越来越重要,有助于诸如新分子设计,蛋白质结构预测和合成规划等任务.
- 在药物发现中,DL面临的一个重大挑战是小数据制度的普遍性,在这种情况下,数据饥饿的算法可能表现不佳.
- 解决这些低数据场景对于实现DL在制药研究中的全部潜力至关重要.
研究的目的:
- 为药物发现提供低数据学习方法近期进展的概述.
- 分析与这些新兴方法相关的具体障碍和优势.
- 预测未来的研究方向,在药物发现中应用低数据学习.
主要方法:
- 关于用于药物发现任务的低数据学习技术的最新文献的审查.
- 分析各种低数据学习策略的性能,局限性和益处.
- 综合发现,以确定趋势和预测未来的研究途径.
主要成果:
- 确定目前在药物发现中正在探索的关键低数据学习方法.
- 评估在这些方法中遇到的实际挑战,例如数据稀缺性和模型通用性.
- 讨论成功的低数据学习应用程序所带来的优势,包括提高效率和新的见解.
结论:
- 低数据学习是推动药物发现中的深度学习应用的关键领域.
- 克服数据限制对于释放AI在制药研究中的全部潜力至关重要.
- 未来的研究应该专注于开发创新的低数据学习技术,以加速药物发现管道.
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