Literature DB >> 29623440

Finding the K best synthesis plans.

Rolf Fagerberg1, Christoph Flamm2, Rojin Kianian1,3,4, Daniel Merkle1, Peter F Stadler5,6,7,8,9,10.   

Abstract

In synthesis planning, the goal is to synthesize a target molecule from available starting materials, possibly optimizing costs such as price or environmental impact of the process. Current algorithmic approaches to synthesis planning are usually based on selecting a bond set and finding a single good plan among those induced by it. We demonstrate that synthesis planning can be phrased as a combinatorial optimization problem on hypergraphs by modeling individual synthesis plans as directed hyperpaths embedded in a hypergraph of reactions (HoR) representing the chemistry of interest. As a consequence, a polynomial time algorithm to find the K shortest hyperpaths can be used to compute the K best synthesis plans for a given target molecule. Having K good plans to choose from has many benefits: it makes the synthesis planning process much more robust when in later stages adding further chemical detail, it allows one to combine several notions of cost, and it provides a way to deal with imprecise yield estimates. A bond set gives rise to a HoR in a natural way. However, our modeling is not restricted to bond set based approaches-any set of known reactions and starting materials can be used to define a HoR. We also discuss classical quality measures for synthesis plans, such as overall yield and convergency, and demonstrate that convergency has a built-in inconsistency which could render its use in synthesis planning questionable. Decalin is used as an illustrative example of the use and implications of our results.

Entities:  

Keywords:  Algorithm; Bond set; Convergency; Decalin; Hypergraph; Hyperpath; Synthesis planning

Year:  2018        PMID: 29623440      PMCID: PMC5887019          DOI: 10.1186/s13321-018-0273-z

Source DB:  PubMed          Journal:  J Cheminform        ISSN: 1758-2946            Impact factor:   5.514


  11 in total

1.  Organic synthesis--art or science?

Authors:  Christoph Rücker; Gerta Rücker; Steven H Bertz
Journal:  J Chem Inf Comput Sci       Date:  2004 Mar-Apr

2.  Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms.

Authors:  Pablo Carbonell; Davide Fichera; Shashi B Pandit; Jean-Loup Faulon
Journal:  BMC Syst Biol       Date:  2012-02-06

Review 3.  Computer-aided organic synthesis.

Authors:  Matthew H Todd
Journal:  Chem Soc Rev       Date:  2005-02-08       Impact factor: 54.564

4.  Empirical Explorations of SYNCHEM.

Authors:  H L Gelernter; A F Sanders; D L Larsen; K K Agarwal; R H Boivie; G A Spritzer; J E Searleman
Journal:  Science       Date:  1977-09-09       Impact factor: 47.728

5.  Computer-assisted design of complex organic syntheses.

Authors:  E J Corey; W T Wipke
Journal:  Science       Date:  1969-10-10       Impact factor: 47.728

6.  Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction.

Authors:  Marwin H S Segler; Mark P Waller
Journal:  Chemistry       Date:  2017-02-22       Impact factor: 5.236

7.  Artificial intelligence in organic synthesis. SST: starting material selection strategies. An application of superstructure search.

Authors:  W T Wipke; D Rogers
Journal:  J Chem Inf Comput Sci       Date:  1984-05

8.  Computer-Assisted Retrosynthesis Based on Molecular Similarity.

Authors:  Connor W Coley; Luke Rogers; William H Green; Klavs F Jensen
Journal:  ACS Cent Sci       Date:  2017-11-16       Impact factor: 14.553

Review 9.  A review of parameters and heuristics for guiding metabolic pathfinding.

Authors:  Sarah M Kim; Matthew I Peña; Mark Moll; George N Bennett; Lydia E Kavraki
Journal:  J Cheminform       Date:  2017-09-15       Impact factor: 5.514

10.  Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.

Authors:  Bowen Liu; Bharath Ramsundar; Prasad Kawthekar; Jade Shi; Joseph Gomes; Quang Luu Nguyen; Stephen Ho; Jack Sloane; Paul Wender; Vijay Pande
Journal:  ACS Cent Sci       Date:  2017-09-05       Impact factor: 18.728

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