Here we show that by taking a sub-set of observations of the chemical space we are able to predict reactions between species that have not been observed yet. In removing all chemical data other than the most fundamental interactions between species we are able to circumvent many of the problems around incomplete or erroneous reaction data plaguing other approaches. Using Markov chain Monte Carlo algorithms we then predict "missing" reactions, first in a test case, to demonstrate its performance before applying it to two case studies . In a previous study we investigated the use of network representations of chemical data obtained from Reaxys (termed "Network of Organic Chemistry" or "NOC") in planning efficient synthesis routes from limonene to paracetamol . This formed the basis of this study in a network containing 161,760 chemical species .