Bioprivileged molecules are biological-derived intermediates from biomass that can be readily converted to a diversity of chemical products including drop-in replacements and novel species[1]. Bridging chemical and biological catalysis by bioprivileged molecules provides a useful and flexible new paradigm for producing biobased chemicals[2]. However, the discovery of bioprivileged molecules such as muconic acid, 5-hydroxymethylfurfural and triacetic acid lactone involves extensive experimental efforts over a long period of time. In this work, we describe a computational framework based on automated network generation that can be used for the identification of bioprivileged molecules. Required input is the chemical file format - structure data format (SDF) of the starting molecules and the reaction rules. The algorithm transforms this information into reactant product relationships, i.e., reactive moiety, reactivity index as a function of number of generations, reaction network, and species properties. Reaction network was generated by applying reaction operators that encode 16 types of catalytic reaction families. On-the-fly estimation of thermodynamics by group contribution method was introduced as a screening criterion to identify the most feasible pathways. Generated species were linked to Pubchem database for identification of novel species and attractiveness evaluation of known products. Applications of the computational framework in screening 29252 C6 species are presented. Through case studies, we demonstrate that the developed computational framework can be used to identify top candidates of bioprivileged molecules and associated feasible chemical pathways to bio-based chemicals, thereby guiding experimentation and computational analysis.

Computational framework for the identification of bioprivileged molecules

255th National Meeting (2018)

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