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AbstractRecent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism. Although this decision must be made based on multifaceted factors, a major one is the suitability of pathways for the endogenous metabolism of a host organism, in part because the efficacy of heterologous biosynthesis is affected by competing endogenous pathways. To address this point, we developed an open-access web server called MRE (metabolic route explorer) that systematically searches for promising heterologous pathways by considering competing endogenous reactions in a given host organism. MRE utilizes reaction Gibbs free energy information. However, 25% of the reactions do not have accurate estimations or cannot be estimated. To address this issue, we developed a method called FC (fingerprint contribution) to provide a more accurate and complete estimation of the reaction free energy. To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. For a given pair of starting and desired compounds in a given chassis organism, MRE ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. The URL of MRE is http://www.cbrc.kaust.edu.sa/mre/. Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. Here, we introduce a machine learning method, referred to as fingerprint contribution (FC), with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reactions. From a large pool of 2D fingerprint-based features, this method systematically selects a small number of relevant ones and uses them to construct a regularized linear model. FC is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx.