Molecular Cavity Topological Representation for Pattern Analysis: A NLP Analogy-Based Word2Vec Method

Dongliang Guo, Qiaoqiao Wang, Meng Liang, Wei Liu, Junlan Nie
<span title="2019-11-29">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="" style="color: black;">International Journal of Molecular Sciences</a> </i> &nbsp;
Cavity analysis in molecular dynamics is important for understanding molecular function. However, analyzing the dynamic pattern of molecular cavities remains a difficult task. In this paper, we propose a novel method to topologically represent molecular cavities by vectorization. First, a characterization of cavities is established through Word2Vec model, based on an analogy between the cavities and natural language processing (NLP) terms. Then, we use some techniques such as dimension
more &raquo; ... and clustering to conduct an exploratory analysis of the vectorized molecular cavity. On a real data set, we demonstrate that our approach is applicable to maintain the topological characteristics of the cavity and can find the change patterns from a large number of cavities.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.3390/ijms20236019</a> <a target="_blank" rel="external noopener" href="">pmid:31795343</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC6928730</a> <a target="_blank" rel="external noopener" href="">fatcat:vxme5vblcrbm7g5en3dh25t7ly</a> </span>
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