Enhancing the students’ perception of machine learning methods-based drug formulation using R_programming educational protocols

• Published in: Future journal of pharmaceutical sciences Vol. 11; no. 1; pp. 102 - 11
• Main Authors: Hathout, Rania M., Ibrahim, Shaimaa S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2025; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Artificial intelligence; Chemoinformatics; Datasets; Education; Eigenvalues; Linear algebra; Machine learning; Medicine; Medicine & Public Health; Pharmaceutical education research; Pharmaceuticals; Pharmacy; Physicochemical properties; Prinicipal component; Python; R programming; Skills; Students; Variables; Pharmaceutical education research; Chemoinformatics; Prinicipal component; Machine learning; R programming
• ISSN: 2314-7253; 2314-7245; 2314-7253
• DOI: 10.1186/s43094-025-00856-w

Background Recently, the need for artificial intelligence (AI) and machine learning (ML) methods in drug development and research is gaining high concern and more grounds. Moreover, providing pharmaceutical and related schools with non-commercial, free-to-use programming languages, software and tools is becoming an unavoidable need. The R programming language can be easily used, through the correct and simplified codes and packages, in conducting unsupervised ML methods, such as principal component analysis (PCA) and hierarchical clustering analysis (HCA), after calculating relevant descriptors of drugs and molecules. Objective The objective of this study was to assess the enhancement of non-computer sciences-based students’ perception of the use of machine learning methods such as PCA and HCA using R-programming in drug formulation. Results Undergraduate students were taught to use R program to derive PCA distinguishable plots such as score, loading and scree, in addition to HCA dendrograms, in the context of developing new pharmaceutical formulations. Surveys conducted pre- and post-teaching the course proved that implementation of such ML methods can help in better understanding and exploring the data, in order to derive meaningful conclusions, and make informed decisions that help develop pharmaceutical formulations of premium quality, with minimal resources consumption. Conclusion We hereby report the easy use of R-programming in applications and activities that introduce undergraduate Pharmaceutical Engineering and Biotechnology students to ML methods. Student surveys showed better student satisfaction and understanding of AI applications in solving pharmaceutical problems. We claim that these students and early_career researchers, who are non-specialists in computer science, can utilize R-programming to perform important pharmaceutical applications through the step-by-step guide and codes provided in this article. Graphical Abstract