Drug Classification in Clinical Pharmacy: Principles,

Applications, and Implications for Patient-Centered Care

Ahmed Ashraf Abdelfatah1,*

1 Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt

Email: ahmedmohamedashraf@std.mans.edu.eg

Received: June 28, 2026 Revised: July 30, 2026 Accepted: September 07, 2026 ⋆ Corresponding author

ABSTRACT

Drug classification in clinical pharmacy is increasingly important for organizing medication knowledge, supporting

therapeutic decisions, and improving patient-centered pharmaceutical care. As healthcare data become more

complex, traditional rule-based classification approaches may be insufficient for interpreting heterogeneous drugrelated

information, including medication histories, molecular descriptors, clinical indicators, adverse-event records,

prescription patterns, and patient-specific risk factors. This review examines how artificial intelligence, machine

learning, deep learning, feature selection, optimization, and decision-support systems contribute to drug classification

in clinical pharmacy. The review discusses the role of intelligent models in medication safety assessment, drug–drug

interaction monitoring, personalized therapy, pharmacy-service planning, and operational medication management.

It also highlights major challenges related to data quality, interpretability, generalizability, workflow integration,

and ethical implementation. Overall, intelligent drug classification can strengthen clinical pharmacy practice when

computational models are transparent, clinically meaningful, and aligned with professional pharmaceutical judgment.

Keywords: Artificial intelligence Drug classification Clinical pharmacy Machine learning Medication safety

1. INTRODUCTION

Drug classification in clinical pharmacy has become an increasingly

important research direction because modern pharmaceutical

care depends on the ability to organize, interpret,

and use drug-related information in a clinically meaningful

manner. In clinical settings, pharmacists and healthcare professionals

are required to evaluate therapeutic indications,

patient characteristics, drug safety profiles, pharmacokinetic

behavior, adverse-event risks, possible drug–drug interactions,

and treatment suitability under conditions of growing

data complexity. Traditional classification approaches, which

often rely on fixed taxonomies, manual interpretation, or rulebased

grouping, can be useful for standard documentation

but may be limited when applied to heterogeneous clinical

data. As healthcare systems move toward more personalized,

evidence-based, and digitally supported decision-making, artificial

intelligence-based classification offers a stronger analytical

foundation for identifying hidden relationships among

drug properties, patient profiles, and therapeutic outcomes

[1]. Figure 1 presents an illustrative graphical summary of

the methodological categories commonly discussed in drug

classification research within clinical pharmacy. The figure

organizes the reviewed methodological directions into

major categories, including deep learning approaches, machine

learning approaches, hybrid or integrated models, optimization

techniques, feature selection methods, fuzzy and

uncertainty-based methods, data-centric approaches, Internet

of Things and sensor-based approaches, operational or

logistics models, and clinical decision-support systems. This

graphical representation helps clarify the methodological orientation

of the review by separating dominant computational