BIBLIOMETRIC ANALYSIS OF RESEARCH PROGRESS ON MACHINE LEARNING FOR IDENTIFICATION OF DRUG CHEMICAL COMPOUNDS

Abstract

Introduction: The development of machine learning-based technology has been utilized in various fields of science ranging from education to social work. The field of health science is one that utilizes the application of machine learning technology, this can be proven by the development of technology that helps in the analysis of treatment and chemical compounds of drugs. The bibliometric analysis aims to provide a visualization of related literature metadata on the topic of machine learning for the analysis of chemical compounds of drugs.

Methods: The method used was literature collection using the Pubmed database with the keywords "(machine learning) AND (drug structure)". Exclusion factors applied for the search were document type, document access, and publication year. The literature obtained was analyzed bibliometrically using Biblioshiny by Bibliometrix. Bibliometric analysis was analyzed in three clusters.

Result: Bibliometric analysis is widely developed by researchers, it can be seen from the number of articles published by researchers. This analysis identified research by 8,301 authors from 79 countries. The largest number of articles relevant to the topic is found in the Scientific Report journal.

Conclusion: The utilization of machine learning has grown rapidly in the last 10 years. Bibliometric analysis with Biblioshiny provides a precise visualization of these developments. The bibliometric analysis identified research trends by 8,301 authors from 79 countries between 2014 and 2024. The highest number of publications per year occurred in 2023 and researcher continuity started from 2020. Keywords and collaboration analysed using node and edge from Louvain scheme.