Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks

<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>

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Interpretable Retrosynthesis Prediction in Two Steps

10.26434/chemrxiv.11869692.v1 ◽

2020 ◽

Cited By ~ 1

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.

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Interpretable Retrosynthesis Prediction in Two Steps

10.26434/chemrxiv.11869692.v2 ◽

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.

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RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

10.26434/chemrxiv.11869692.v5 ◽

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>

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RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

10.26434/chemrxiv.11869692 ◽

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>

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RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

10.26434/chemrxiv.11869692.v4 ◽

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>

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RetroXpert: Decompose Retrosynthesis Prediction like A Chemist

10.26434/chemrxiv.11869692.v3 ◽

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>

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IPOMDP-Net: A Deep Neural Network for Partially Observable Multi-Agent Planning Using Interactive POMDPs

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016062 ◽

2019 ◽

Vol 33 ◽

pp. 6062-6069 ◽

Cited By ~ 1

Author(s):

Yanlin Han ◽

Piotr Gmytrasiewicz

Keyword(s):

Neural Network ◽

Network Architecture ◽

State Of The Art ◽

Neural Computing ◽

Neural Network Architecture ◽

Markov Decision ◽

Planning Algorithm ◽

Multi Agent ◽

Partially Observable ◽

Multi Agent Planning

This paper introduces the IPOMDP-net, a neural network architecture for multi-agent planning under partial observability. It embeds an interactive partially observable Markov decision process (I-POMDP) model and a QMDP planning algorithm that solves the model in a neural network architecture. The IPOMDP-net is fully differentiable and allows for end-to-end training. In the learning phase, we train an IPOMDP-net on various fixed and randomly generated environments in a reinforcement learning setting, assuming observable reinforcements and unknown (randomly initialized) model functions. In the planning phase, we test the trained network on new, unseen variants of the environments under the planning setting, using the trained model to plan without reinforcements. Empirical results show that our model-based IPOMDP-net outperforms the other state-of-the-art modelfree network and generalizes better to larger, unseen environments. Our approach provides a general neural computing architecture for multi-agent planning using I-POMDPs. It suggests that, in a multi-agent setting, having a model of other agents benefits our decision-making, resulting in a policy of higher quality and better generalizability.

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Optimal neural network architecture selection: effects on computer-aided detection of mammographic microcalcifications

10.1117/12.467095 ◽

2002 ◽

Cited By ~ 1

Author(s):

Metin N. Gurcan ◽

Heang-Ping Chan ◽

Berkman Sahiner ◽

Lubomir M. Hadjiiski ◽

Nicholas Petrick ◽

...

Keyword(s):

Neural Network ◽

Network Architecture ◽

Selection Effects ◽

Computer Aided Detection ◽

Neural Network Architecture ◽

Computer Aided

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Robustness of Neural Network Emulations of Radiative Transfer Parameterizations in a State-of-the-Art General Circulation Model

10.5194/gmd-2021-114 ◽

2021 ◽

Author(s):

Alexei Belochitski ◽

Vladimir Krasnopolsky

Keyword(s):

Neural Network ◽

Radiative Transfer ◽

Network Architecture ◽

General Circulation ◽

State Of The Art ◽

Circulation Model ◽

Set Design ◽

Neural Network Architecture ◽

Parametric Change ◽

Model Components

Abstract. The ability of Machine-Learning (ML) based model components to generalize to the previously unseen inputs, and the resulting stability of the models that use these components, has been receiving a lot of recent attention, especially when it comes to ML-based parameterizations. At the same time, ML-based emulators of existing parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural-network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art GCM are robust with respect to the substantial structural and parametric change in the host model: when used in two seven month-long experiments with the new model, they not only remain stable, but generate realistic output. Aspects of neural network architecture and training set design potentially contributing to stability of ML-based model components are discussed.

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