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Background: In the modern era, the growth of scientific literature presents a daunting challenge for researchers to keep informed of advancements across multiple disciplines. Objective: We apply natural language processing (NLP) and embedding learning concepts to design PubDigest, a tool that combs PubMed literature, aiming to pinpoint potential drugs that could be repurposed. Methods: Using NLP, especially term associations through word embeddings, we explored unrecognized relationships between drugs and diseases. To illustrate the utility of PubDigest, we focused on chronic thromboembolic pulmonary hypertension (CTEPH), a rare disease with an overall limited number of scientific publications. Results: Our literature analysis identified key clinical features linked to CTEPH by applying term frequency-inverse document frequency (TF-IDF) scoring, a technique measuring a term's significance in a text corpus. This allowed us to map related diseases. One standout was venous thrombosis (VT), which showed strong semantic links with CTEPH. Looking deeper, we discovered potential repurposing candidates for CTEPH through large-scale neural network-based contextualization of literature and predictive modeling on both the CTEPH and the VT literature corpora to find novel, yet unrecognized associations between the two diseases. Alongside the anti-thrombotic agent caplacizumab, benzofuran derivatives were an intriguing find. In particular, the benzofuran derivative amiodarone displayed potential anti-thrombotic properties in the literature. Our in vitro tests confirmed amiodarone's ability to reduce platelet aggregation significantly by 68% (p = 0.02). However, real-world clinical data indicated that CTEPH patients receiving amiodarone treatment faced a significant 15.9% higher mortality risk (p<0.001). Conclusions: While NLP offers an innovative approach to interpreting scientific literature, especially for drug repurposing, it is crucial to combine it with complementary methods like in vitro testing and real-world evidence. Our exploration with benzofuran derivatives and CTEPH underscores this point. Thus, blending NLP with hands-on experiments and real-world clinical data can pave the way for faster and safer drug repurposing approaches, especially for rare diseases like CTEPH. Author summary: We tackled the challenge of keeping up with the ever-growing scientific literature. We focused on leveraging the power of natural language processing (NLP) to work through extensive literature data, targeting the discovery of new drug applications. Our tool, PubDigest, applies advanced NLP techniques and scans vast amounts of research abstracts from PubMed to uncover links between drugs and diseases (Fig 1). Our primary case study presents chronic thromboembolic pulmonary hypertension (CTEPH), a rare but life-threatening condition. Employing PubDigest, a notable discovery was the potential use of caplacizumab or benzofuran derivatives like amiodarone in treating CTEPH, suggested by their anti-thrombotic properties. However, we didn't rely solely on computational analysis. Lab experiments, toxicity prediction, and clinical data were essential to validate these findings. While amiodarone showed promise in laboratory tests, real-world clinical data indicated increased mortality risks in CTEPH patients treated with it. This highlighted the crucial need to complement computational discoveries with practical, real-world evaluations. Our research shows the value of combining computational tools with traditional methods in medical research. This approach accelerates drug discovery and emphasizes a comprehensive view, especially for rare diseases. Through this work, we aim to contribute a novel, efficient pipeline for drug discovery that opens new doors in medical treatment and research. [ABSTRACT FROM AUTHOR]