Literature DB >> 35438992

Algorithm for the Pruning of Synthesis Graphs.

Gergely Zahoránszky-Kőhalmi1, Nikita Lysov1, Ilia Vorontcov1, Jeffrey Wang1, Jeyaraman Soundararajan1, Dimitrios Metaxotos1, Biju Mathew1, Rafat Sarosh1, Samuel G Michael1, Alexander G Godfrey1.   

Abstract

Synthesis route planning is in the core of chemical intelligence that will power the autonomous chemistry platforms. In this task, we rely on algorithms to generate possible synthesis routes with the help of retro- and forward-synthetic approaches. Generated synthesis routes can be merged into a synthesis graph which represents theoretical pathways to the target molecule. However, it is often required to modify a synthesis graph due to typical constraints. These constraints might include "undesirable substances", e.g., an intermediate that the chemist does not favor or substances that might be toxic. Consequently, we need to prune the synthesis graph by the elimination of such undesirable substances. Synthesis graphs can be represented as directed (not necessarily acyclic) bipartite graphs, and the pruning of such graphs in the light of a set of undesirable substances has been an open question. In this study, we present the Synthesis Graph Pruning (SGP) algorithm that addresses this question. The input to the SGP algorithm is a synthesis graph and a set of undesirable substances. Furthermore, information for substances is provided as metadata regarding their availability from the inventory. The SGP algorithm operates with a simple local rule set, in order to determine which nodes and edges need to be eliminated from the synthesis graph. In this study, we present the SGP algorithm in detail and provide several case studies that demonstrate the operation of the SGP algorithm. We believe that the SGP algorithm will be an essential component of computer aided synthesis planning.

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Year:  2022        PMID: 35438992      PMCID: PMC9093600          DOI: 10.1021/acs.jcim.1c01202

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   6.162


  23 in total

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Review 3.  Computer-Assisted Synthetic Planning: The End of the Beginning.

Authors:  Sara Szymkuć; Ewa P Gajewska; Tomasz Klucznik; Karol Molga; Piotr Dittwald; Michał Startek; Michał Bajczyk; Bartosz A Grzybowski
Journal:  Angew Chem Int Ed Engl       Date:  2016-04-08       Impact factor: 15.336

4.  Context Aware Data-Driven Retrosynthetic Analysis.

Authors:  Christos A Nicolaou; Ian A Watson; Mark LeMasters; Thierry Masquelin; Jibo Wang
Journal:  J Chem Inf Model       Date:  2020-04-24       Impact factor: 4.956

5.  Machine Learning in Computer-Aided Synthesis Planning.

Authors:  Connor W Coley; William H Green; Klavs F Jensen
Journal:  Acc Chem Res       Date:  2018-05-01       Impact factor: 22.384

Review 6.  Bipartite graphs in systems biology and medicine: a survey of methods and applications.

Authors:  Georgios A Pavlopoulos; Panagiota I Kontou; Athanasia Pavlopoulou; Costas Bouyioukos; Evripides Markou; Pantelis G Bagos
Journal:  Gigascience       Date:  2018-04-01       Impact factor: 6.524

7.  A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

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Journal:  J Chem Inf Model       Date:  2022-01-20       Impact factor: 4.956

8.  Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction.

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9.  CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration.

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Journal:  J Cheminform       Date:  2020-09-01       Impact factor: 5.514

10.  Towards a methodology for validation of centrality measures in complex networks.

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Journal:  PLoS One       Date:  2014-04-07       Impact factor: 3.240

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