This paper introduces the concept of an isomer network based on the reaction step counts between pairs of isomers as an alternative means to view and analyze isomer space. The computation of isomer networks is computationally expensive with respect to both run time and memory. Accordingly, this paper focuses on the design of algorithms to compute isomer networks and their analysis on structurally diverse subsets of isomers of nicotine, tyrosine, and phenmetrazine generated using molecular quantum number nearest neighbors. An analysis correlating isomer networks to extended connectivity fingerprints is also provided.

This article presents software applications that have been built upon a modular, open-source, reaction mapping library that can be used in both cheminformatics and bioinformatics research. We first describe the theoretical underpinnings and modular architecture of the core software library. We then describe two applications that have been built upon that core. The first is a generic reaction viewer and mapper, and the second classifies reactions according to rules that can be modified by end users with little or no programming skills.

Automated reaction mapping is an important tool in cheminformatics where it may be used to classify reactions or validate reaction mechanisms. The reaction mapping problem is known to be NP-Hard and may be formulated as an optimization problem. In this article, we present four algorithms that continue to obtain optimal solutions to this problem, but with significantly improved runtimes over the previous Constructive Count Vector (CCV) algorithm. Our algorithmic improvements include (i) the use of a fast (but not 100% accurate) canonical labeling algorithm, (ii) name reuse (i.e., storing intermediate results rather than recomputing), and (iii) an incremental approach to canonical name computation. The time to map the reactions from the Kegg/Ligand database previously took over 2 days using CCV, but now it takes fewer than 4 hours to complete. Experimental results on chemical reaction databases demonstrate our 2-CCV FDN MS algorithm usually performs over fifteen times faster than previous automated reaction mapping algorithms. ACM Reference Format:Kouri, T. M. and Mehta, D. P. 2013. Faster reaction mapping through improved naming techniques. ACM J.

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