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MHNfs:促进低数据药物发现的背景生物活性预测

Johannes Schimunek¹, Sohvi Luukkonen¹, Günter Klambauer¹

¹ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, A-4040 Linz, Austria.

Journal of chemical information and modeling

|April 30, 2025

概括

药物发现面临着数据稀缺的挑战. 一个新的应用程序MHNfs使用少量射击学习来预测有限数据的分子活动,加速候选者识别.

科学领域:

计算化学是一种计算化学.
机器学习在药物发现中的作用
生物信息学是一种生物信息学.

背景情况:

药物发现越来越多地利用计算和机器学习 (ML) 方法.
这些方法的一个重大障碍是缺乏高质量的数据.
解决数据局限性对于推进计算药物发现至关重要.

研究的目的:

介绍MHNfs,这是一个用于在低数据设置中进行分子活动预测的应用程序.
为研究人员提供一个可访问的工具,以便在药物发现中利用少数人学习.
为了使用最小已知的分子数据来实现精确的活动预测.

主要方法:

开发了MHNfs应用程序,结合了最先进的几次射击学习模型.
使用MHNfs模型,该模型在FS-Mol基准数据集上表现强.
模拟现实世界药物发现场景,通过将PubChem生物测试调整为少量预测任务.

主要成果:

MHNfs模型在一些射击活动预测任务中取得了强的表现.
该应用程序为用户提供了一个直观的界面,以获得分子活动预测.
使用调整的PubChem生物测试进行的评估证实了该应用在数据不足的情况下的有效性.

结论:

MHNfs为部署先进的几次射击学习模型提供了一种简化和可访问的解决方案.
该应用程序有效地解决了在计算药物发现中数据稀缺的挑战.
MHNfs是加速识别新药候选药物的宝贵工具.

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