Navigating around Patented Routes by Preserving Specific Motifs along Computer-Planned Retrosynthetic Pathways

Molga, Karol; Dittwald, Piotr; Grzybowski, Bartosz A.

doi:10.1016/j.chempr.2018.12.004

Cited by 52 publications

(49 citation statements)

References 43 publications

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“…Synthia (formerly Chematica), one of the most well-known, expertencoded, template-based retrosynthetic analysis tools, is a commercial program developed by Grzybowski and coworkers. 9,[13][14][15][16][17][18] This tool uses a manually collected knowledge database containing about 70 000 hand-encoded reaction transformation rules. 18 Based on human knowledge of organic synthesis and the encoding of organic rules over a period of more than 15 years, Synthia has been validated experimentally as an efficient toolkit for complex products recently.…”

Section: Introductionmentioning

confidence: 99%

“…9,[13][14][15][16][17][18] This tool uses a manually collected knowledge database containing about 70 000 hand-encoded reaction transformation rules. 18 Based on human knowledge of organic synthesis and the encoding of organic rules over a period of more than 15 years, Synthia has been validated experimentally as an efficient toolkit for complex products recently. 16 However, it would not be practical to manually collect all the knowledge of a BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China.…”

Section: Introductionmentioning

confidence: 99%

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Automatic retrosynthetic route planning using template-free models

et al. 2020

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Retrosynthetic route planning can be considered a rule-based reasoning procedure. The possibilities for each transformation are generated based on collected reaction rules, and then potential reaction routes are recommended by various optimization algorithms. Although there has been much progress in computer-assisted retrosynthetic route planning and reaction prediction, fully data-driven automatic retrosynthetic route planning remains challenging. Here we present a template-free approach that is independent of reaction templates, rules, or atom mapping, to implement automatic retrosynthetic route planning. We treated each reaction prediction task as a data-driven sequence-to-sequence problem using the multi-head attention-based Transformer architecture, which has demonstrated power in machine translation tasks. Using reactions from the United States patent literature, our end-to-end models naturally incorporate the global chemical environments of molecules and achieve remarkable performance in top-1 predictive accuracy (63.0%, with the reaction class provided) and top-1 molecular validity (99.6%) in one-step retrosynthetic tasks. Inspired by the success rate of the one-step reaction prediction, we further carried out iterative, multi-step retrosynthetic route planning for four case products, which was successful. We then constructed an automatic data-driven end-to-end retrosynthetic route planning system (AutoSynRoute) using Monte Carlo tree search with a heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The end-to-end model for reaction task prediction can be easily extended to larger or customer-requested reaction databases. Our study presents an important step in realizing automatic retrosynthetic route planning.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic retrosynthetic route planning using template-free models

et al. 2020

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“…1,2 Computer aided synthesis planning (CASP) has long been investigated as a means for predicting how to make a given compound. [3][4][5] However, despite recent progress in the field, [6][7][8][9][10][11][12] synthetic planning tools based on neural network classifiers have failed to recognize reactions that are infrequently used or rare, due to to the heavily biased datasets available. 7,13 As such, CASP tools have not yet focused on the synthesis of ring systems, the reactions for which often fall within the noise of datasets in question.…”

Section: Introductionmentioning

confidence: 99%

“…The ability to deconstruct ring systems in novel ways, offers medicinal and process chemists alike, the opportunity to explore a wider range of chemical space and create more efficient synthetic routes, thereby leading to a competitive advantage. 11 Ring systems are key scaffold components in medicinal chemistry, and are fundamental motifs to a number of drugs on the market today. 14 They vary greatly in nature -ring systems can be saturated, unsaturated, polycyclic and range in sizes from small heterocyclic rings to large macrocycles.…”

Section: Introductionmentioning

confidence: 99%

‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

Thakkar

Selmi²,

Reymond³

et al. 2020

Preprint

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Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ uses a data-driven approach to enable the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate the performance of the neural network on a range of ring fragments from the ZINC and DrugBank databases and highlight its potential for incorporation into computer aided synthesis planning tools. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes.

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“…1,2 Computer aided synthesis planning (CASP) has long been investigated as a means for predicting how to make a given compound. [3][4][5] However, despite recent progress in the field, [6][7][8][9][10][11][12] synthetic planning tools based on neural network classifiers have failed to recognize reactions that are infrequently used or rare, due to the heavily biased datasets available. 7,13 As called 'Rings of the Future' for which we examine the predictive performance.…”

Section: Introductionmentioning

confidence: 99%

‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space

Thakkar¹,

Selmi²,

Reymond³

et al. 2019

Preprint

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Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ enables the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate its performance on a range of ring fragments from the ZINC database and highlight its potential for incorporation into computer aided synthesis planning tools. Additionally, we generate a multi-label dataset using bipartite reaction graphs on which we train ‘Ring Breaker’ to model the relationship between one ring fragment and the multiple reactions recorded for its synthesis in the dataset; we thereby overcome the single-label approaches previously used. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes.

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Navigating around Patented Routes by Preserving Specific Motifs along Computer-Planned Retrosynthetic Pathways

Cited by 52 publications

References 43 publications

Automatic retrosynthetic route planning using template-free models

Automatic retrosynthetic route planning using template-free models

‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space

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