Before revision:  INtERAcT implements a new metric acting on the vector space of word representations to estimate an interaction score between two molecules . We show that the proposed metric outperforms other metrics at the task of identifying known molecular interactions reported in publicly available databases for different cancer types.
After revision:  INtERAcT implements a new metric acting on the vector space of word representations to estimate an interaction score between two molecules in the space where the corresponding words are embedded. We demonstrate the power of INtERAcT by reconstructing the molecular pathways associated to 10 different cancer types using a corpus of disease-specific articles for each cancer type. We evaluate INtERAcT using STRING database as a benchmark, and show that our metric outperforms currently adopted approaches for similarity computation at the task of identifying known molecular interactions reported in publicly available databases for different cancer types.