This software repository provides an approach for exploring and evaluating literature-based document-to-document (doc-2-doc) recommendations using the Word2Vec technique. The methodology generates word-level embeddings using the Word2Vec model and leverages the Word Mover's Distance (WMD) to assess document similarity. It utilizes the RELISH Corpus, a biomedical literature dataset curated by experts, containing pairwise document assessments. The software workflow includes training Word2Vec models on a predefined training set, followed by the evaluation of doc-2-doc recommendations using a distinct test set. Additionally, Optuna is integrated for hyperparameter optimization of the trained Word2Vec models using the validation dataset, enabling fine-tuning of model performance. This work used deNBI resources and therefore was supported by the de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) and ELIXIR-DE (Forschungszentrum Jülich and W-de.NBI-001, W-de.NBI-004, W-de.NBI-008, W-de.NBI-010, W-de.NBI-013, W-de.NBI-014, W-de.NBI-016, W-de.NBI-022).