Molecular Graph Enhanced Transformer for Retrosynthesis Prediction

Mao, Kelong; Zhao, Peilin; Xu, Tingyang; Rong, Yu; Xiao, Xi; Huang, Junzhou

doi:10.1101/2020.03.05.979773

Cited by 9 publications

(9 citation statements)

References 12 publications

(5 reference statements)

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“…Previous graph-based MO models are criticized for fragile training processes and limited chemical diversity. For this reason, we adopted a graph enhanced Transformer (GET) for generating optimized molecules with the input of a source molecule. − …”

Section: Methodsmentioning

confidence: 99%

Meta Learning for Low-Resource Molecular Optimization

Wang

Zheng

Chen

et al. 2021

J. Chem. Inf. Model.

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The goal of molecular optimization (MO) is to discover molecules that acquire improved pharmaceutical properties over a known starting molecule. Despite many recent successes of new approaches for MO, these methods were typically developed for particular properties with rich annotated training examples. Thus, these approaches are difficult to implement in real scenes where only a small amount of pharmaceutical data is usually available due to the expense and significant effort required for the data collection. Here, we propose a new approach, Meta-MO, for molecular optimization with a handful of training samples based on the wellrecognized first-order meta-learning algorithms. By using a set of meta tasks with rich training samples, Meta-MO trains a meta model through the meta-learning optimization and adapts the learned model to new low-resource MO tasks. Meta-MO was shown to consistently outperform several pretraining and multitask training procedures, providing an average improvement in the success rate of 4.3% on a large-scale bioactivity data set with diverse target variations. We also observed that Meta-MO resulted in the best performing models across fine-tuning sets with only dozens of samples. To the best of our knowledge, this is the first study to apply meta learning to MO tasks. More importantly, such a strategy could be further extended to many low-resource scenarios in realworld drug design.

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Section: Methodsmentioning

confidence: 99%

Meta Learning for Low-Resource Molecular Optimization

Wang

Zheng

Chen

et al. 2021

J. Chem. Inf. Model.

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“…Recent few years have seen deep graph learning (DGL) based on graph neural networks (GNNs) making remarkable progress in a variety of important areas, ranging from business scenarios such as finance (e.g., fraud detection and credit modeling) [28,144,9,67], e-commerce (e.g., recommendation system) [126,85], drug discovery and advanced material discovery [41,134,100,82,83]. Despite the progress, applying various DGL algorithms to real-world applications faces a Inherent Noise D train = (A + a , X + x , Y + y ) [164], [80], [87], [93], [72], [24] [101], [115] Distribution shift P train (G, Y ) = P test (G, Y )…”

Section: Trustworthy Graph Learningmentioning

confidence: 99%

“…In the past few years, DGL is becoming an active frontier of deep learning with an exponential growth of research. With advantages in modeling graph-structured data, DGL has achieved remarkable progress in many important areas, ranging from finance (e.g., fraud detection and credit modeling) [28,144,9,67], e-commerce (e.g., recommendation system) [126,85], drug discovery and advanced material discovery [41,134,100,82,83].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Wu¹,

Li²,

Yu³

et al. 2022

Preprint

Self Cite

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Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite these progresses, how to ensure various deep graph learning algorithms behave in a socially responsible manner and meet regulatory compliance requirements becomes an emerging problem, especially in risk-sensitive domains. Trustworthy graph learning (TwGL) aims to solve the above problems from a technical viewpoint. In contrast to conventional graph learning research which mainly cares about model performance, TwGL considers various reliability and safety aspects of the graph

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“…This simple strategy has been demonstrated to be a powerful method in terms of graph expression and structure information extraction 45 . Details regarding graph transformers can be found elsewhere 46 .…”

Section: Molecular Sequence Feature Extractionmentioning

confidence: 99%

Artificial intelligence-based gas chromatography-olfactometry for sensory evaluation of key compounds in food ingredients

Shang

Liu

Tang

et al. 2022

Preprint

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The prediction of structure-odor-relationship (SOR) by deep neural networks (DNN) via the structural features of odorants has attracted great attention during the past decade. Due to the limited knowledge on binding mechanism between odorant molecules and olfactory receptors, however, it is not sure what kind of structural features play the most important role in smell recognition. Here, diverse DNNs, including molecular parameters neural network, molecular graphic convolution neural network (MG-CNN), molecular graph transformer neural network, and atom interaction neural network, were used to extract the structure features of molecules and to predict the categorized odor perception. The experimental results demonstrated that an MG-CNN combined with a multi-label DNN classifier produced the best results, with an area under the receiver operating characteristic curve and F1-score of 0.877±0.028 and 0.726±0.028, respectively. This is the first systematic study for molecular structure features extracted by different deep neural network and their predictive effect for SOR. We believe that these insights regarding the use of DNN-based odorant molecular feature extraction for odor sensory identification will be useful for introducing biologically interpretable artificial intelligence into olfactometry, and thus contribute to our understanding of the mechanisms underlying human olfaction.

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Molecular Graph Enhanced Transformer for Retrosynthesis Prediction

Cited by 9 publications

References 12 publications

Meta Learning for Low-Resource Molecular Optimization

Meta Learning for Low-Resource Molecular Optimization

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Artificial intelligence-based gas chromatography-olfactometry for sensory evaluation of key compounds in food ingredients

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