TransG-net: transformer and graph neural network based multi-modal data fusion network for molecular properties prediction

Zhang, Taohong; Chen, Saian; Wulamu, Aziguli; Guo, Xuxu; Li, Qianqian; Zheng, Han

doi:10.1007/s10489-022-04351-0

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…[24][25][26][27][28] An exemplary promise is reected in its power for data analysis. 17,[29][30][31][32][33][34][35][36] In a recent study, a Mass2SMILES model based on Transformer was employed to predict functional groups and SMILES descriptors from the high-resolution MS/MS spectra, 29 showing mean square errors (MSEs) of 0.0001 and 0.24 for the functional groups and SMILES descriptors, respectively. Another Transformer model was trained to predict molecular structures from the 1 H/ 13 C NMR spectra, showing a Top-1 accuracy of 67%.…”

Section: Introductionmentioning

confidence: 99%

An interpretable and transferrable vision transformer model for rapid materials spectra classification

Chen,

Xie,

et al. 2024

Digital Discovery

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Section: Introductionmentioning

confidence: 99%

An interpretable and transferrable vision transformer model for rapid materials spectra classification

Chen,

Xie,

et al. 2024

Digital Discovery

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show abstract

“…License: CC BY-NC-ND 4.0 for data analysis. 17,[29][30][31][32][33][34][35][36] In a recent study, a Mass2SMILES model based on Transformer was employed to predict functional groups and SMILES descriptors from the high-resolution MS/MS spectra, 29 showing mean square errors (MSE) of 0.0001 and 0.24 for the functional groups and SMILES descriptors, respectively. Another Transformer model was trained to predict molecular structures from the 1 H/ 13 C NMR spectra, showing a Top-1 accuracy of 67 %.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable and Transferrable Vision Transformer Model for Rapid Materials Spectra Classification

Chen,

Xie,

et al. 2023

Preprint

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Rapid analysis of materials characterization spectra is pivotal for preventing accumulation of unwieldy datasets, thus accelerating subsequent decision-making. However, current methods heavily rely on experience and domain knowledge, which not only proves tedious but also is hard to keep up with the pace of data acquisition. In this context, we introduce a transferable Vision Transformer (ViT) model for identification of materials from their spectra, including XRD and FTIR. First, an optimal ViT model was trained to predict metal organic frameworks (MOFs) from their XRD spectra. It attains prediction accuracies of 70%, 93%, and 94.9% for Top-1, Top-3, and Top-5, respectively, and a shorter training time of 269 seconds in comparison to a convolutional neural network model. The dimension reduction and attention weight map underline its adeptness at capturing relevant features in the XRD spectra for determining the prediction outcome. Moreover, the model can be transferred to a new one for prediction of organic molecules from their FTIR spectra, attaining remarkable Top-1, Top-3, and Top-5 prediction accuracies of 84%, 94.1%, and 96.7%, respectively. The introduced ViT based model would set a new revenue to handling diverse types of spectroscopic data, thus expediting the materials characterization processes.

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Transformer technology in molecular science

Jiang,

Ke,

Chen

et al. 2024

WIREs Comput Mol Sci

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A transformer is the foundational architecture behind large language models designed to handle sequential data by using mechanisms of self‐attention to weigh the importance of different elements, enabling efficient processing and understanding of complex patterns. Recently, transformer‐based models have become some of the most popular and powerful deep learning (DL) algorithms in molecular science, owing to their distinctive architectural characteristics and proficiency in handling intricate data. These models leverage the capacity of transformer architectures to capture complex hierarchical dependencies within sequential data. As the applications of transformers in molecular science are very widespread, in this review, we only focus on the technical aspects of transformer technology in molecule domain. Specifically, we will provide an in‐depth investigation into the algorithms of transformer‐based machine learning techniques in molecular science. The models under consideration include generative pre‐trained transformer (GPT), bidirectional and auto‐regressive transformers (BART), bidirectional encoder representations from transformers (BERT), graph transformer, transformer‐XL, text‐to‐text transfer transformer, vision transformers (ViT), detection transformer (DETR), conformer, contrastive language‐image pre‐training (CLIP), sparse transformers, and mobile and efficient transformers. By examining the inner workings of these models, we aim to elucidate how their architectural innovations contribute to their effectiveness in processing complex molecular data. We will also discuss promising trends in transformer models within the context of molecular science, emphasizing their technical capabilities and potential for interdisciplinary research. This review seeks to provide a comprehensive understanding of the transformer‐based machine learning techniques that are driving advancements in molecular science.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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TransG-net: transformer and graph neural network based multi-modal data fusion network for molecular properties prediction

Cited by 5 publications

References 43 publications

An interpretable and transferrable vision transformer model for rapid materials spectra classification

An interpretable and transferrable vision transformer model for rapid materials spectra classification

An Interpretable and Transferrable Vision Transformer Model for Rapid Materials Spectra Classification

Transformer technology in molecular science

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