scholarly journals Interpretable Retrosynthesis Prediction in Two Steps

2020 ◽  
Author(s):  
Chaochao Yan ◽  
Qianggang Ding ◽  
Shuangjia Zheng ◽  
Jinyu Yang ◽  
Yang Yu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Building Blocks ◽  
Computer Aided ◽  
Free Model ◽  
Benchmark Datasets ◽  
Previous State ◽  
Target Molecules ◽  
Template Free ◽  
Synthetic Routes ◽  
High Diversity

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and lacks interpretability behind their predictions. In this study, we devise a novel template-free model for retrosynthetic expansion by automating the procedure that chemistsusedtodo. Our method plans synthesis in two steps, by first identifying the potential disconnection bonds of the molecule graph with a graph neural network and thereafter generating synthons according to the identified disconnection bonds of the target molecule graph, and then predicting the associated reactants SMILES based on the obtained synthons with a reactant prediction model. While outperforming previous state-of-the-art baselines by a significant margin on the benchmark datasets, our model also provides predictions with high diversity and chemically reasonable interpretation.

scholarly journals Interpretable Retrosynthesis Prediction in Two Steps

2020 ◽  
Author(s):  
Chaochao Yan ◽  
Qianggang Ding ◽  
Peilin Zhao ◽  
Shuangjia Zheng ◽  
Jinyu Yang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Building Blocks ◽  
Computer Aided ◽  
Free Model ◽  
Benchmark Datasets ◽  
Previous State ◽  
Target Molecules ◽  
Template Free ◽  
Synthetic Routes ◽  
High Diversity

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and lacks interpretability behind their predictions. In this study, we devise a novel template-free model for retrosynthetic expansion by automating the procedure that chemistsusedtodo. Our method plans synthesis in two steps, by first identifying the potential disconnection bonds of the molecule graph with a graph neural network and thereafter generating synthons according to the identified disconnection bonds of the target molecule graph, and then predicting the associated reactants SMILES based on the obtained synthons with a reactant prediction model. While outperforming previous state-of-the-art baselines by a significant margin on the benchmark datasets, our model also provides predictions with high diversity and chemically reasonable interpretation.

scholarly journals Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks

2019 ◽  
Author(s):  
Shuangjia Zheng ◽  
Jiahua Rao ◽  
Zhongyue Zhang ◽  
Jun Xu ◽  
Yuedong Yang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
State Of The Art ◽  
Neural Network Architecture ◽  
Training Set ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Target Molecules ◽  
Template Free ◽  
Synthetic Routes

<p><a>Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations of reactants and the products. Coupled with a neural network-based syntax corrector, our method achieves an accuracy of 59.0% on a standard benchmark dataset, which increases >21% over other deep learning methods, and >6% over template-based methods. More importantly, our method shows an accuracy 1.7 times higher than other state-of-the-art methods for compounds not appearing in the training set.</a></p>

scholarly journals Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks

2019 ◽  
Author(s):  
Shuangjia Zheng ◽  
Jiahua Rao ◽  
Zhongyue Zhang ◽  
Jun Xu ◽  
Yuedong Yang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
State Of The Art ◽  
Neural Network Architecture ◽  
Training Set ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Target Molecules ◽  
Template Free ◽  
Synthetic Routes

<p><a>Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations of reactants and the products. Coupled with a neural network-based syntax corrector, our method achieves an accuracy of 59.0% on a standard benchmark dataset, which increases >21% over other deep learning methods, and >6% over template-based methods. More importantly, our method shows an accuracy 1.7 times higher than other state-of-the-art methods for compounds not appearing in the training set.</a></p>

scholarly journals RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

2020 ◽  
Author(s):  
Chaochao Yan ◽  
Qianggang Ding ◽  
Peilin Zhao ◽  
Shuangjia Zheng ◽  
Jinyu Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Reaction Center ◽  
Organic Synthesis ◽  
State Of The Art ◽  
Building Blocks ◽  
Prediction Methods ◽  
Generation Model ◽  
Synthesis Planning ◽  
Target Molecules ◽  
Template Free

<div>Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.</div><div>However, most of them are cumbersome and lack interpretability about their predictions.</div><div>In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.</div><div>Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. </div><div>While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.</div>

scholarly journals RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

2020 ◽  
Author(s):  
Chaochao Yan ◽  
Qianggang Ding ◽  
Peilin Zhao ◽  
Shuangjia Zheng ◽  
Jinyu Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Reaction Center ◽  
Organic Synthesis ◽  
State Of The Art ◽  
Building Blocks ◽  
Prediction Methods ◽  
Generation Model ◽  
Synthesis Planning ◽  
Target Molecules ◽  
Template Free

<div>Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.</div><div>However, most of them are cumbersome and lack interpretability about their predictions.</div><div>In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.</div><div>Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. </div><div>While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.</div>

scholarly journals RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

2020 ◽  
Author(s):  
Chaochao Yan ◽  
Qianggang Ding ◽  
Peilin Zhao ◽  
Shuangjia Zheng ◽  
Jinyu Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Reaction Center ◽  
Organic Synthesis ◽  
State Of The Art ◽  
Building Blocks ◽  
Prediction Methods ◽  
Generation Model ◽  
Synthesis Planning ◽  
Target Molecules ◽  
Template Free

<div>Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.</div><div>However, most of them are cumbersome and lack interpretability about their predictions.</div><div>In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.</div><div>Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. </div><div>While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.</div>

scholarly journals RetroXpert: Decompose Retrosynthesis Prediction like A Chemist

2020 ◽  
Author(s):  
Chaochao Yan ◽  
Qianggang Ding ◽  
Peilin Zhao ◽  
Shuangjia Zheng ◽  
Jinyu Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Reaction Center ◽  
Organic Synthesis ◽  
State Of The Art ◽  
Building Blocks ◽  
Prediction Methods ◽  
Generation Model ◽  
Synthesis Planning ◽  
Target Molecules ◽  
Template Free

<div>Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.</div><div>However, most of them are cumbersome and lack interpretability about their predictions.</div><div>In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.</div><div>Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. </div><div>While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.</div>

scholarly journals Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

2020 ◽  
Vol 10 (20) ◽  
pp. 7201
Author(s):  
Xiao-Xia Yin ◽  
Lihua Yin ◽  
Sillas Hadjiloucas
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Information Fusion ◽  
State Of The Art ◽  
General Structure ◽  
Building Blocks ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Dce Mri ◽  
Computer Aided

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.

scholarly journals A Domain Generalization Perspective on Listwise Context Modeling

2019 ◽  
Vol 33 ◽  
pp. 5965-5972 ◽  
Author(s):  
Lin Zhu ◽  
Yihong Chen ◽  
Bowen He
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
State Of The Art ◽  
Learning To Rank ◽  
Context Modeling ◽  
Ranking Problem ◽  
Neural Architecture ◽  
Benchmark Datasets ◽  
Previous State ◽  
Latent Representations

As one of the most popular techniques for solving the ranking problem in information retrieval, Learning-to-rank (LETOR) has received a lot of attention both in academia and industry due to its importance in a wide variety of data mining applications. However, most of existing LETOR approaches choose to learn a single global ranking function to handle all queries, and ignore the substantial differences that exist between queries. In this paper, we propose a domain generalization strategy to tackle this problem. We propose QueryInvariant Listwise Context Modeling (QILCM), a novel neural architecture which eliminates the detrimental influence of inter-query variability by learning query-invariant latent representations, such that the ranking system could generalize better to unseen queries. We evaluate our techniques on benchmark datasets, demonstrating that QILCM outperforms previous state-of-the-art approaches by a substantial margin.

scholarly journals Knowledge Base Question Answering with Topic Units

2019 ◽  
Author(s):  
Yunshi Lan ◽  
Shuohang Wang ◽  
Jing Jiang
Keyword(s):  
Natural Language Processing ◽  
Reinforcement Learning ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
Entity Linking ◽  
Named Entities ◽  
Benchmark Datasets ◽  
Previous State

Knowledge base question answering (KBQA) is an important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities found in a question as the starting points in the KB to search for answers to the question. However, relying only on entity linking to look for answer candidates may not be sufficient. In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB. We use a generation-and-scoring approach to gradually refine the set of topic units. Furthermore, we use reinforcement learning to jointly learn the parameters for topic unit linking and answer candidate ranking in an end-to-end manner. Experiments on three commonly used benchmark datasets show that our method consistently works well and outperforms the previous state of the art on two datasets.

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