Multi-Cover Persistence (MCP)-based machine learning for polymer property prediction.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Yipeng Zhang, Cong Shen, Kelin Xia
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引用次数: 0

Abstract

Accurate and efficient prediction of polymers properties is crucial for polymer design. Recently, data-driven artificial intelligence (AI) models have demonstrated great promise in polymers property analysis. Even with the great progresses, a pivotal challenge in all the AI-driven models remains to be the effective representation of molecules. Here we introduce Multi-Cover Persistence (MCP)-based molecular representation and featurization for the first time. Our MCP-based polymer descriptors are combined with machine learning models, in particular, Gradient Boosting Tree (GBT) models, for polymers property prediction. Different from all previous molecular representation, polymer molecular structure and interactions are represented as MCP, which utilizes Delaunay slices at different dimensions and Rhomboid tiling to characterize the complicated geometric and topological information within the data. Statistic features from the generated persistent barcodes are used as polymer descriptors, and further combined with GBT model. Our model has been extensively validated on polymer benchmark datasets. It has been found that our models can outperform traditional fingerprint-based models and has similar accuracy with geometric deep learning models. In particular, our model tends to be more effective on large-sized monomer structures, demonstrating the great potential of MCP in characterizing more complicated polymer data. This work underscores the potential of MCP in polymer informatics, presenting a novel perspective on molecular representation and its application in polymer science.

基于多覆盖持久性 (MCP) 的聚合物性能预测机器学习。
准确、高效地预测聚合物特性对聚合物设计至关重要。最近,数据驱动的人工智能(AI)模型在聚合物特性分析中展现出了巨大的前景。尽管取得了巨大进步,但所有人工智能驱动模型的一个关键挑战仍然是如何有效地表示分子。在此,我们首次引入了基于多覆盖持久性(MCP)的分子表征和特征化。我们基于 MCP 的聚合物描述符与机器学习模型,特别是梯度提升树(GBT)模型相结合,用于聚合物特性预测。与以往所有的分子表示方法不同,聚合物分子结构和相互作用以 MCP 表示,利用不同维度的 Delaunay 切片和 Rhomboid tiling 来描述数据中复杂的几何和拓扑信息。生成的持久性条形码的统计特征被用作聚合物描述符,并进一步与 GBT 模型相结合。我们的模型已在聚合物基准数据集上进行了广泛验证。结果发现,我们的模型优于传统的基于指纹的模型,其准确性与几何深度学习模型相似。特别是,我们的模型对大尺寸单体结构更有效,这表明 MCP 在表征更复杂的聚合物数据方面具有巨大潜力。这项工作强调了 MCP 在聚合物信息学中的潜力,为分子表征及其在聚合物科学中的应用提出了一个新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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