3 citations · 3 across the 5 of their papers we have counts for
5 papers
DrugGen 2: A disease-aware language model for enhancing drug discovery
Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami +4
Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influ…
Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages
Mohammadreza Ghaffarzadeh-Esfahani, Nahid Yousefian, Ebrahim Heidari-Farsani +4
Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP). This study evalu…
AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting
Mohammadreza Ghaffarzadeh-Esfahani, Yousof Gheisari
Adeno-associated viruses (AAVs) are promising vectors for gene therapy, but their native serotypes face limitations in tissue tropism, immune evasion, and production efficiency. En…
DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model
Mohammadreza Ghaffarzadeh-Esfahani, Ali Motahharynia, Nahid Yousefian +3
Drug discovery is a complex and resource-intensive process, making early prediction of approval outcomes critical for optimizing research investments. While classical machine learn…
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
Mohammadreza Ghaffarzadeh-Esfahani, Mahdi Ghaffarzadeh-Esfahani, Arian Salahi-Niri +39
This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimension…