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arXiv 2205.03339physics.chem-phcs.LGphysics.bio-phq-bio.BM
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用机器学习在分子系统之间传递化学和能量知识

Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning

University of Manitoba(曼尼托巴大学);Università della Svizzera italiana (USI)(瑞士意大利语大学);University of Exeter(埃克塞特大学);University of Naples “Federico II”(那不勒斯“费德里科二世”大学)

Sajjad Heydari, Stefano Raniolo, Lorenzo Livi, Vittorio Limongelli

摘要:

预测分子系统的结构和能量特性是分子模拟的基本任务之一,它在化学,生物学和医学中有应用。在过去的十年中,机器学习算法的出现影响了各种任务的分子模拟,包括原子系统的属性预测。在本文中,我们提出了一种新的方法,从简单的分子系统获得的知识转移到一个更复杂的,拥有显着更大的原子数量和自由度。特别是,我们专注于高和低自由能状态的分类。我们的方法依赖于利用(i)分子的新型超图表示,编码用于表征构象势能的所有相关信息,以及(ii)用于处理和预测这种超图结构数据的新型消息传递和池化层。尽管问题很复杂,但我们的结果显示,从三丙氨酸到十丙氨酸系统的迁移学习具有显著的AUC为0.92。此外,我们表明,非常相同的迁移学习方法可以用于分组,在一个无监督的方式,各种二级结构的十-丙氨酸在集群具有类似的自由能值。我们的研究代表了一个概念证明,即可以设计分子系统的可靠迁移学习模型,为预测生物相关系统的结构和能量特性铺平道路。

英文摘要:

Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has use cases in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms has impacted on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, possessing a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy states. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing the potential energy of a conformation, and (ii) novel message passing and pooling layers for processing and making predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable AUC of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the very same transfer learning approach can be used to group, in an unsupervised way, various secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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