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 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 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 Edge-Nodes Representation Neural Machine for Link Prediction

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 12 ◽  
Author(s):  
Guangluan Xu ◽  
Xiaoke Wang ◽  
Yang Wang ◽  
Daoyu Lin ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Formation Mechanism ◽  
Link Prediction ◽  
State Of The Art ◽  
Prediction Methods ◽  
Topological Features ◽  
Novel Method ◽  
Fully Connected ◽  
Two Sides

Link prediction is a task predicting whether there is a link between two nodes in a network. Traditional link prediction methods that assume handcrafted features (such as common neighbors) as the link’s formation mechanism are not universal. Other popular methods tend to learn the link’s representation, but they cannot represent the link fully. In this paper, we propose Edge-Nodes Representation Neural Machine (ENRNM), a novel method which can learn abundant topological features from the network as the link’s representation to promote the formation of the link. The ENRNM learns the link’s formation mechanism by combining the representation of edge and the representations of nodes on the two sides of the edge as link’s full representation. To predict the link’s existence, we train a fully connected neural network which can learn meaningful and abundant patterns. We prove that the features of edge and two nodes have the same importance in link’s formation. Comprehensive experiments are conducted on eight networks, experiment results demonstrate that the method ENRNM not only exceeds plenty of state-of-the-art link prediction methods but also performs very well on diverse networks with different structures and characteristics.

scholarly journals Improved protein docking by predicted interface residues

2021 ◽  
Author(s):  
Gabriele Pozzati ◽  
Petras Kundrotas ◽  
Arne Elofsson
Keyword(s):  
State Of The Art ◽  
Protein Docking ◽  
Prediction Methods ◽  
Scoring Functions ◽  
Protein Interface ◽  
High Importance ◽  
Docking Algorithm ◽  
Docking Protocol ◽  
Template Free ◽  
Interface Prediction

ABSTRACTScoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.

scholarly journals RetroPrime: A Chemistry-Inspired and Transformer-based Method for Retrosynthesis Predictions

2020 ◽  
Author(s):  
Xiaorui Wang ◽  
Jiezhong Qiu ◽  
Yuquan Li ◽  
Guangyong Chen ◽  
Huanxiang Liu ◽  
...  
Keyword(s):  
Organic Synthesis ◽  
Large Scale ◽  
State Of The Art ◽  
Chemical Space ◽  
Full Dataset ◽  
Template Free ◽  
Leaving Groups ◽  
Real Practice ◽  
Published Result

Retrosynthesis prediction is a crucial task for organic synthesis. In this work, we propose a template-free and Transformer-based method dubbed RetroPrime, integrating chemists’ retrosynthetic strategy of (1) decomposing a molecule into synthons then (2) generating reactants by attaching leaving groups. These two steps are accomplished with versatile Transformer models, respectively. While RetroPrime performs competitively against all state-of-the art models on the standard USPTO-50K dataset, it manifests remarkable generalizability and outperforms the only published result by a non-trivial margin of 4.8% for the Top-1 accuracy on the large-scale USPTO-full dataset. It is known that outputs of Transformer-based retrosynthesis model tend to suffer from insufficient diversity and high invalidity. These problems may limit the potential of Transformer-based methods in real practice, yet no prior works address both issues simultaneously. RetroPrime is designed to tackle these challenges. Finally, we provide convincing results to support the claim that RetromPrime can more effectively generalize across chemical space.

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