Recognition of Molecular Structure of Phosphonium Salts from the Visual Appearance of Material with Deep Learning Can Reveal Subtle Homologs,Small

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Recognition of Molecular Structure of Phosphonium Salts from the Visual Appearance of Material with Deep Learning Can Reveal Subtle Homologs
Small ( IF 13.0 ) Pub Date : 2024-09-10 , DOI: 10.1002/smll.202403423
Daniil A Boiko ₁ , Daria M Arkhipova ₁ , Valentine P Ananikov ₁

Affiliation

Determining molecular structures is foundational in chemistry and biology. The notion of discerning molecular structures simply from the visual appearance of a material remained almost unthinkable until the advent of machine learning. This paper introduces a pioneering approach bridging the visual appearance of materials (both at the micro- and nanostructural levels) with traditional chemical structure analysis methods. Quaternary phosphonium salts are opted as the model compounds, given their significant roles in diverse chemical and medicinal fields and their ability to form homologs with only minute intermolecular variances. This research results in the successful creation of a neural network model capable of recognizing molecular structures from visual electron microscopy images of the material. The performance of the model is evaluated and related to the chemical nature of the studied chemicals. Additionally, unsupervised domain transfer is tested as a method to use the resulting model on optical microscopy images, as well as test models trained on optical images directly. The robustness of the method is further tested using a complex system of phosphonium salt mixtures. To the best of the authors' knowledge, this study offers the first evidence of the feasibility of discerning nearly indistinguishable molecular structures.

中文翻译：

通过深度学习从材料的视觉外观中识别鏻盐的分子结构可以揭示细微的同源物

确定分子结构是化学和生物学的基础。在机器学习出现之前，仅从材料的视觉外观中辨别分子结构的概念几乎是不可想象的。本文介绍了一种开创性的方法，将材料的视觉外观（在微结构和纳米结构水平上）与传统的化学结构分析方法联系起来。季鏻盐被选为模型化合物，因为它们在不同的化学和医学领域中发挥着重要作用，并且它们能够形成仅具有微小分子间差异的同系物。这项研究成功创建了一个神经网络模型，该模型能够从材料的视觉电子显微镜图像中识别分子结构。对模型的性能进行评估，并与所研究化学品的化学性质相关。此外，无监督域转移作为一种在光学显微镜图像上使用结果模型的方法，以及直接在光学图像上训练的测试模型。使用复杂的鏻盐混合物系统进一步测试了该方法的稳定性。据作者所知，这项研究提供了第一个证据，证明辨别几乎无法区分的分子结构是可行的。

更新日期：2024-09-10

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