We investigate the validity of this approximation for each reaction in the dataset by means of the "neighbor test," i.e. if the resulting x C alkene i of eq. ( ) leads closest to the true alkene embedding or not within the total compounded set of all alkenes (= 2192, see Table ). The neighbor test is analogous to the one carried out for the original word vector analogies where the word embedding for "King" -"Man" + "Woman" came out nearest to the vector for "Queen." Here, the average oxidation vector serves the same role as the vector for ' -"Man" + "Woman"' which transformed the word "King" to "Queen," and similarly "policeman" to "policewoman" and "boy" to "girl". In this case, the average oxidation vector can map 62.8% of reactant alkanes to be nearest their true product alkene C-embedding at the reaction center, see Table . This points towards a highly structured space, as more than 1 in 2 vectors are mapped exactly to the correct alkene using an average estimate reaction vector, opposed to a probability of 1 in N alkenes (1/2192, see Table ) if this would be random over a uniform distribution. In other words, the average ⟨alkane oxidation⟩ vector does not map to just any of the 2192 available alkenes, but lands exactly on the correct one 62.8% of the time. A noteworthy observation is that a 100% neighbor test can be achieved if the removal of hydrogen is performed without any subsequent force field optimization. Such discrepancy and the role of geometry optimization will be discussed later.