Exploring the Integration of Molecular Modeling and Computational Pharmacology: A Comprehensive Study on Ligand-Receptor Interaction Analysis

Abstract

Computational pharmacology has emerged as a pivotal field in drug discovery, leveraging molecular modeling techniques to predict ligand-receptor interactions and streamline therapeutic development. Despite advancements, the subjective experiences of researchers navigating the complexities of computational tools and interdisciplinary collaboration remain underexplored. This study uses a phenomenological approach to explore how researchers interpret and integrate molecular simulations within the broader context of drug discovery workflows. The research uncovers the lived experiences of ten researchers, focusing on challenges related to technical complexity, interdisciplinary dynamics, and validation of computational results. Data were collected through in-depth interviews and analyzed thematically to capture shared meanings and interpretations.  Findings reveal that researchers face significant obstacles in aligning computational predictions with experimental realities, emphasizing the importance of collaborative problem-solving and tailored training. These insights provide a nuanced understanding of the human dimensions underpinning computational pharmacology, bridging gaps in prior research that focused primarily on technical performance metrics. The study highlights the critical role of subjective experiences in advancing computational methodologies, offering a foundation for future research aimed at integrating human-centered approaches into drug discovery frameworks.