Department of pharmaceutics, Channabasweshwar college of pharmacy (Degree), Latur
Artificial intelligence (AI) has emerged as a transformative technology in pharmaceutical sciences, providing innovative solutions to challenges associated with conventional drug research and development. Traditional pharmaceutical approaches are often time-consuming, costly, and associated with high attrition rates. The integration of AI-based tools, particularly machine learning (ML) and deep learning (DL) techniques, has enabled data-driven predictions, automation, and efficient handling of large and complex datasets. This review summarizes the fundamental concepts of artificial intelligence and discusses its applications across major pharmaceutical domains, including drug discovery, formulation development, pharmacokinetics and pharmacodynamics, clinical trials, and pharmacovigilance. Additionally, current challenges, ethical considerations, and regulatory issues related to AI adoption are highlighted. Future perspectives emphasize the role of explainable AI and personalized medicine. Overall, artificial intelligence is reshaping pharmaceutical research and holds significant promise for accelerating the development of safe and effective medicines .
Pharmaceutical sciences play a critical role in the discovery, development, manufacturing, and safe use of medicinal products. Conventional drug development strategies rely heavily on experimental screening and trial-and-error approaches, which are resource-intensive and frequently result in high failure rates [1]. With increasing complexity in drug targets and the exponential growth of biomedical data, traditional methodologies are often insufficient.Recent advancements in computational power and data analytics have facilitated the widespread adoption of artificial intelligence (AI) in pharmaceutical research. AI refers to computational systems capable of simulating human intelligence, including learning, reasoning, and decision-making [2]. Techniques such as machine learning, deep learning, and natural language processing enable efficient analysis of large datasets generated during pharmaceutical research. This review aims to provide a comprehensive overview of artificial intelligence and critically examine its applications, challenges, and future potential in pharmaceutical sciences [3,4].
2. Overview of Artificial Intelligence
Artificial intelligence encompasses a broad range of computational techniques designed to replicate human cognitive functions. Among these, machine learning (ML) enables systems to learn patterns from data and improve predictive performance without explicit programming [5] .Deep learning (DL), a subset of ML, employs multilayered neural networks capable of modeling highly complex and nonlinear relationships, making it particularly useful for molecular modeling and image-based analysis [6].Natural language processing (NLP) facilitates the extraction of meaningful information from unstructured textual data, including scientific publications, clinical reports, and adverse drug reaction databases [7]. The major AI techniques commonly employed in pharmaceutical sciences and their applications are summarized in Table 1.
Table 1. Common Artificial Intelligence Techniques Used in Pharmaceutical Sciences
|
AI Technique
|
Description |
Major Pharmaceutical Applications |
|
Machine Learning (ML) |
Algorithms that learn patterns from datasets |
Drug discovery, ADMET prediction, formulation optimization |
|
Deep Learning (DL) |
Multilayer neural networks for complex modeling |
Molecular modeling, image-based screening |
|
Natural Language Processing (NLP) |
Computational analysis of textual data |
Literature mining, pharmacovigilance |
|
Artificial Neural Networks (ANN) |
Brain-inspired computational models |
Dissolution prediction, PK modeling |
|
Support Vector Machines (SVM) |
Classification and regression algorithms |
Drug–target interaction prediction |
Source: Compiled by the authors based on published literature [1–5] .
3. Applications of Artificial Intelligence in Pharmaceutical Sciences
3.1 AI in Drug Discovery
Drug discovery is a complex, multistep process involving target identification, hit discovery, lead optimization, and preclinical evaluation. Artificial intelligence has significantly accelerated this process by enabling virtual screening of large chemical libraries and predicting drug–target interactions with high accuracy [2,5]. ML models analyze molecular descriptors and biological datasets to identify promising candidates, thereby reducing experimental costs and development timelines.AI-driven de novo drug design further enables the generation of novel chemical entities with optimized pharmacokinetic and pharmacodynamic properties, contributing to improved success rates during early-stage drug discovery [6,8].
3.2 AI in Pharmaceutical Formulation Development
Formulation development focuses on optimizing drug stability, bioavailability, and patient compliance. AI tools assist formulation scientists by predicting the influence of formulation and process variables on critical quality attributes such as particle size, dissolution rate, and drug release profiles [9]. Machine learning models support Quality by Design (QbD) approaches by identifying critical material attributes and process parameters, minimizing trial-and-error experimentation.
3.3 AI in Pharmacokinetics and Pharmacodynamics
Pharmacokinetic (PK) and pharmacodynamic (PD) studies are essential for understanding drug absorption, distribution, metabolism, excretion, and therapeutic response. AI-based models predict PK/PD parameters using preclinical and clinical datasets, reducing dependence on extensive animal and human studies [7]. These approaches also support dose optimization and individualized therapy, facilitating personalized medicine [10].
3.4 AI in Clinical Trials
Clinical trials represent one of the most expensive and time-consuming phases of drug development. Artificial intelligence improves clinical trial efficiency by optimizing study design, enhancing patient recruitment, and predicting trial outcomes using real-world data and electronic health records [3,10]. The role of AI across various stages of pharmaceutical development is summarized in Table 2.
Table 2. Application of Artificial Intelligence Across Different Stages of Pharmaceutical Development
|
Development Stage
|
Conventional Approach |
|
||
|
Drug discovery |
Experimental screening |
Faster virtual screening and cost reduction |
||
|
Formulation development |
Trial-and-error methods |
Predictive formulation optimization |
||
|
Pharmacokinetics |
|
In silico PK prediction |
||
|
Clinical trials |
|
AI-assisted patient selection |
||
|
Pharmacovigilance |
|
Automated safety signal detection |
3.5 AI in Pharmacovigilance and Drug Safety
Pharmacovigilance focuses on the detection and prevention of adverse drug reactions to ensure patient safety. AI-based systems analyze large volumes of post-marketing surveillance data, including spontaneous reports and social media content, enabling early detection of safety signals [7]. NLP techniques play a critical role in extracting relevant information from unstructured data sources, improving the efficiency and accuracy of drug safety monitoring [3].
4. Challenges and Limitations of AI in Pharmaceutical Sciences
Despite its advantages, the application of artificial intelligence in pharmaceutical sciences faces several challenges. The reliability of AI predictions depends heavily on the availability of high-quality and representative datasets [5]. Additionally, the lack of transparency in complex AI models, often described as the “black-box” problem, raises concerns regarding interpretability and regulatory acceptance.Ethical issues related to data privacy, algorithmic bias, and accountability must also be addressed. Moreover, regulatory frameworks governing AI-based pharmaceutical tools are still evolving, which may limit their widespread adoption [4,10]. The major advantages and limitations of AI applications are summarized in Table 3.
Table 3. Advantages and Limitations of Artificial Intelligence in Pharmaceutical Sciences
|
Advantages
|
Limitations |
|
Reduces development time and cost |
Requires large, high-quality datasets |
|
Improves prediction accuracy |
Limited model interpretability |
|
Supports personalized medicine |
Ethical and privacy concerns |
|
Efficient handling of big data |
Regulatory uncertainty |
Source: Authors’ analysis based on recent literature [1,4,10].
FUTURE PERSPECTIVES
The future of artificial intelligence in pharmaceutical sciences is highly promising. Advances in explainable AI are expected to improve transparency and regulatory acceptance. Integration of AI with real-world evidence, digital health technologies, and omics data will further enhance drug discovery and development processes [1,6]. Interdisciplinary collaboration will be essential for maximizing the potential of AI-driven pharmaceutical research.
CONCLUSION
Artificial intelligence has emerged as a powerful and versatile tool in pharmaceutical sciences, offering innovative solutions across drug discovery, formulation development, pharmacokinetics, clinical trials, and pharmacovigilance. Although challenges related to data quality, ethics, and regulation persist, ongoing technological advancements are expected to address these limitations. The integration of AI into pharmaceutical research holds significant potential to improve efficiency, reduce costs, and enhance patient outcomes, making AI a cornerstone of future pharmaceutical innovation [1–4].
REFERENCE
Akash kawale*, Krishna patil, Applications of Artificial Intelligence in Pharmaceutical Sciences: A Review, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 2, 295-299. https://doi.org/10.5281/zenodo.18464414
10.5281/zenodo.18464414
