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Modeling Organic Chemistry and Planning Organic Synthesis

20 pagesPublished: December 18, 2015


Organic Synthesis is a computationally challenging practical problem concerned with constructing a target molecule from a set of initially available molecules via chemical reactions. This paper demonstrates how organic synthesis can be formulated as a planning problem in Artificial Intelligence, and how it can be explored using the state-of-the-art domain independent planners.
To this end, we develop a methodology to represent chemical molecules and generic reactions in PDDL 2.2, a version of the standardized Planning Domain Definition Language popular in AI. In our model, derived predicates define common functional groups and chemical classes in chemistry, and actions
correspond to generic chemical reactions. We develop a set of benchmark problems. Since PDDL is supported as an input language by many modern planners, our benchmark can be subsequently useful for
empirical assessment of the performance of various state-of-the-art planners.

Keyphrases: knowledge representation, organic chemistry synthesis problem, reasoning about action

In: Georg Gottlob, Geoff Sutcliffe and Andrei Voronkov (editors). GCAI 2015. Global Conference on Artificial Intelligence, vol 36, pages 176--195

BibTeX entry
  author    = {Arman Masoumi and Megan Antoniazzi and Mikhail Soutchanski},
  title     = {Modeling Organic Chemistry and Planning Organic Synthesis},
  booktitle = {GCAI 2015. Global Conference on Artificial Intelligence},
  editor    = {Georg Gottlob and Geoff Sutcliffe and Andrei Voronkov},
  series    = {EPiC Series in Computing},
  volume    = {36},
  pages     = {176--195},
  year      = {2015},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/493z}}
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