Thematic Evolution and Institutional Dynamics of Deep Learning Assisted GPCR Modulators Research: A Decade-Long Bibliometric Analysis (2015-2025)
Keywords:
Deep Learning; GPCR; G Protein-Coupled Receptor; Bibliometric Analysis; Drug Discovery; Allosteric Modulators; ChemoinformaticsAbstract
The present investigation is constituted by the objective assessment of the scientific performance, the underlying social structure, and the conceptual evolution characteristic of research pertaining to Deep Learning (DL)-Assisted GPCR Modulators during the decennial period spanning 2015 through 2025. Given the critically pivotal function of G Protein-Coupled Receptors (GPCRs) in the pharmacological endeavor of drug discovery, the integration of DL methodologies is deemed to afford a vital pathway for the surmounting of traditional screening impediments. A quantitative bibliometric analysis was executed upon 132 documented items retrieved from the Scopus database and subsequently processed via the dedicated Bibliometrix/Biblioshiny software suite. Observational data delineate an exponential augmentation in output, characterized by a Compound Annual Growth Rate (CAGR) measuring 29.73%, and demonstrative of a collaborative axis dominantly positioned between the United States and China. Conceptually, the principal Motor Themes identified are "Algorithm," "Proteins," and "Prediction." The thematic evolution evinces a distinct paradigm shift in the research emphasis, progressing from the development of general computational methodologies toward specific pharmacological and clinical objectives relating to Drug Targeting. It is concluded that this specialized domain has achieved a substantial degree of maturation, transitioning from a foundational preoccupation with algorithmic concerns to a primary focus upon application-driven, outcomes-based pharmacological desiderata.
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