Artificial Intelligence in Pharmacology: From Drug Discovery to Personalized Medicine and Future Horizons

Artificial intelligence (AI) has rapidly evolved from a theoretical concept in computer science into a transformative tool across healthcare, including pharmacology. The capacity of AI to process complex, multidimensional datasets, recognize hidden patterns, and generate predictive insights makes it uniquely suited for pharmacological applications where the traditional “trial-and-error” approach is time-consuming, costly, and often limited by human bias. The convergence of computational power, availability of biomedical big data, and advances in machine learning algorithms has opened new possibilities for drug discovery, personalized therapy, clinical trial optimization, pharmacovigilance, and regulatory science (1,2) Pharmacology has historically relied on experimental and observational science to determine drug mechanisms, safety, and efficacy. With the exponential growth of data from genomics, proteomics, metabolomics, imaging, electronic health records (EHRs), and real-world evidence, the need for robust analytical tools has become urgent. AI provides solutions by integrating diverse datasets to accelerate therapeutic innovations while maintaining precision. As Ferguson et al. (2001) emphasized in their early review on technology integration in pharmacology, innovation must be grounded in rigorous evaluation, ethical considerations, and alignment with clinical needs.

This principle continues to guide the incorporation of AI into modern pharmacological research and practice. The integration of AI is not simply a matter of automating existing processes. Instead, it represents a paradigm shift—transforming how pharmacologists conceptualize targets, design molecules, predict pharmacokinetics, monitor adverse effects, and tailor treatments to individual patients. Despite the enthusiasm, challenges persist, including issues of transparency, validation, reproducibility, bias, and regulation (3,4) . Thus, a critical review of AI in pharmacology must balance optimism with scientific caution, addressing both current achievements and unresolved barriers. This article aims to provide a comprehensive account of AI in pharmacology: tracing its conceptual foundations, describing how AI is integrated across drug discovery, clinical pharmacology, and pharmacovigilance, critically appraising current achievements, identifying systemic limitations, and projecting future directions such as blockchain-enabled AI, digital twins, and federated learning.

1. FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE

1.1 Defining Artificial Intelligence

Artificial intelligence refers to the simulation of human cognitive functions such as learning, reasoning, problem-solving, and decision-making by machines. In the healthcare context, AI systems are designed to analyze data, recognize patterns, and generate actionable insights. Unlike traditional statistical approaches, AI can handle nonlinear, high-dimensional datasets without explicit preprogrammed rules (5).

AI encompasses several branches:

• Machine Learning (ML): Algorithms that learn from data and improve over time.
• Deep Learning (DL): Subset of ML using neural networks with multiple layers, capable of complex feature extraction.
• Reinforcement Learning (RL): Algorithms that optimize decision-making based on trial-and-error feedback.
• Natural Language Processing (NLP): Techniques that analyze unstructured biomedical texts and clinical narratives (6,7)

1.2 Historical Development of AI in Medicine

The early AI systems in healthcare were expert systems (e.g., MYCIN in the 1970s), which relied on rule-based logic to guide clinical decisions. However, these systems were limited by their rigidity and inability to scale with increasing data. The 21st century witnessed a paradigm shift with the advent of machine learning and deep learning, supported by improved computational resources and access to large biomedical datasets (8)

In pharmacology, early applications included computer-assisted drug design and predictive toxicology. Today, AI spans the entire pharmacological spectrum, from molecular docking to predicting real-world drug effectiveness and safety.

1.3 Core AI Methodologies Relevant to Pharmacology

Supervised Learning: Models trained on labeled datasets to predict outcomes (e.g., drug response prediction using genetic markers).

Unsupervised Learning: Identifies hidden clusters and associations in unlabeled datasets (e.g., novel adverse event detection).

Deep Neural Networks: Including convolutional neural networks (CNNs) for image-based drug screening and recurrent neural networks (RNNs) for sequential data such as longitudinal EHRs. The introduction of transformer models (9) and their derivatives have enabled breakthroughs in sequence analysis, protein folding, and biomedical NLP.

Generative Models: Generative adversarial networks (GANs) and variational autoencoders (VAEs) are applied to de novo drug design, virtual screening, and missing data imputation (10).

Reinforcement Learning: Increasingly important in precision dosing and adaptive clinical trial design (11).

1.4 Data Sources for AI in Pharmacology

AI in pharmacology thrives on data availability. Key sources include:

Omics data (genomics, proteomics, metabolomics, transcriptomics): Driving pharmacogenomics and systems pharmacology.

Biomedical imaging (radiomics and pathomics): Supporting drug response prediction and biomarker discovery (12).

Electronic Health Records (EHRs): Providing longitudinal data on drug efficacy, safety, and patient adherence (13).

Pharmacovigilance Databases: AI-assisted monitoring of spontaneous reports and social media mining for adverse drug reactions (14,15) .

Clinical Trials and Real-World Evidence: AI applications in protocol optimization and regulatory submissions (16,17).

1.5 Distinguishing AI from Conventional Computational Pharmacology

Conventional computational pharmacology tools, such as molecular docking and quantitative structure–activity relationship (QSAR) models, are deterministic and hypothesis-driven . AI, in contrast, is hypothesis-generating, able to uncover unexpected associations and emergent patterns without explicit rules. For example, whereas QSAR models require pre-specified descriptors, deep learning approaches can autonomously learn relevant features from raw chemical structures (18) .

1.6 Ethical Foundations in AI Development

Before discussing integration, it is crucial to highlight the ethical framework guiding AI deployment. As the World Medical Association (19) emphasized, AI should be viewed as “augmented intelligence,” assisting clinicians and pharmacologists rather than replacing them. Transparency, fairness, and accountability are foundational to responsible AI.

The conceptual development of artificial intelligence in medicine has its origins in early statistical learning and clinical decision-support systems. Early applications of regression-based models and pattern recognition in (20) biomedical research demonstrated the potential of computers to mimic aspects of human reasoning in healthcare contexts. These pioneering steps laid the foundation for modern AI, particularly when integrated with large-scale electronic health data.

The introduction of deep learning in healthcare marked a major inflection point. For example, neural architectures trained on longitudinal patient records demonstrated that EHR data could predict hospitalization risk, mortality, and drug side effects with remarkable accuracy (8,21) . These studies underscored the transition from handcrafted features to end-to-end learning, an approach that is now central to pharmacological applications.

In parallel, computational chemistry and pharmacology benefitted from machine learning methods even before “AI” became a popular term.(22) Techniques such as quantitative structure–activity relationship (QSAR) modeling were among the first to highlight the ability of algorithms to extract hidden patterns linking molecular descriptors with pharmacological outcomes (23). Advances in QSAR later merged with deep generative models, producing drug design strategies that go beyond traditional high-throughput screening.

The emphasis on interpretability and biological plausibility remains critical. As Handelman et al. (2018) argue, AI models must not only achieve predictive accuracy but also generate mechanistic insights to be truly transformative in pharmacology. For this reason, early experiences with predictive toxicology and metabolic modeling continue to inform how pharmacologists evaluate the validity of newer neural-network–based models.

Figure 1: AI Applications Across the Drug Discovery and Development Pipeline. (24)

Table 1 (concise). AI methods and where they’re used in pharmacology

2.1 AI in Drug Discovery and Development

Drug discovery traditionally involves labor-intensive experimental screening of thousands of compounds, with high costs and low success rates. Artificial intelligence offers a paradigm shift by enabling rational drug design, target identification, virtual screening, and toxicity prediction.

One of the most transformative applications is de novo drug design, where generative models propose novel molecular structures with optimized pharmacological properties. Deep learning–based platforms such as DeepAffinity and DeepTox have demonstrated high accuracy in predicting compound–protein binding affinities and toxicity, respectively0 (18,25).  AI-powered structure prediction, epitomized by AlphaFold (26) has revolutionized protein modeling, enabling pharmacologists to design drugs against previously “undruggable” targets.

Drug repurposing represents another AI success story. During the COVID-19 pandemic, AI-driven approaches accelerated candidate selection, including baricitinib as a treatment option (27). Knowledge graph–based approaches and systems pharmacology pipelines systematically integrate heterogeneous biomedical data to prioritize repurposing candidates (28).

Moreover, AI enhances virtual high-throughput screening by learning from chemical libraries to predict hit-to-lead optimization. In oncology, for instance, Bayesian and neural network–based predictive models are used to forecast drug–target interactions and resistance patterns (29). These applications underline how AI compresses the timeline and cost of drug development while improving success rates (30,31)

2.2 AI in Clinical Pharmacology

2.2.1 Precision Dosing and PK/PD Modeling

Therapeutic drug monitoring and individualized dosing are longstanding goals of clinical pharmacology. AI, particularly reinforcement learning and hybrid pharmacokinetic (PK)–machine learning models, has enabled model-informed precision dosing (MIPD). Studies on vancomycin, isavuconazole, tacrolimus, and mycophenolic acid demonstrate that hybrid ML–PK models outperform conventional Bayesian approaches in predicting drug exposure (32–34).

For example, reinforcement learning frameworks have been applied to precision dosing of chemotherapeutics, where the algorithm adjusts dose regimens dynamically based on patient response data (11). Such models reduce toxicity risk while maximizing therapeutic benefit, especially in populations with high variability such as pediatrics or transplant patients.(35)

2.2.2 Pharmacogenomics and AI-Driven Prediction

AI has also accelerated progress in pharmacogenomics, enabling the interpretation of high-dimensional genetic data to predict drug response. Transfer learning approaches allow models trained on common variants to extrapolate predictions to rare variants of pharmacogenes such as CYP2D6 (36). Similarly, AI-driven classifiers have been applied to predict metabolizer phenotypes directly from sequencing data, bridging the gap between genomic knowledge and actionable clinical pharmacology (37) .

These developments are essential for tailoring treatment to individual patients, reducing the risks of adverse reactions, and improving therapeutic outcomes in precision medicine.

2.3 AI in Pharmacovigilance and Drug Safety

Pharmacovigilance relies on spontaneous adverse event reporting systems, which are often underreported and delayed. AI-based text mining and NLP approaches now enable real-time monitoring of EHRs, clinical notes, and social media to detect adverse drug events (ADEs).

For instance, AI systems deployed within the UK’s Yellow Card Scheme analyze millions of reports to flag potential drug safety signals earlier than manual review (38). DeepADEMiner, an NLP pipeline, mines Twitter for adverse drug effect mentions, normalizing them into standard MedDRA terminology (15)(39).

In addition, machine learning models support causality assessment, distinguishing true drug-related events from confounding factors (14). Automated pharmacovigilance not only enhances efficiency but also democratizes patient safety by including diverse data sources beyond formal clinical reports.(40)

2.4 AI in Clinical Trials and Regulatory Science

2.4.1 Clinical Trial Design and Optimization

AI has profound implications for clinical trial efficiency. Predictive modelling can optimize patient selection, sample size, and early termination criteria ((41) .For example, machine learning classifiers have been used to predict trial discontinuation risk, aiding sponsors in resource allocation (42).

SPIRIT-AI and CONSORT-AI extensions provide frameworks for designing and reporting AI-enabled interventions in clinical trials (16,17). These guidelines are critical for ensuring transparency and reproducibility of AI-assisted clinical evidence.

One of the earliest and most promising avenues for AI integration in pharmacology has been the use of electronic health record (EHR) mining. Large-scale EHR datasets, when paired with natural language processing, allow researchers to identify adverse drug events, drug–drug interactions, and off-label uses that might otherwise go unnoticed. Such approaches represent a shift from reactive to proactive pharmacovigilance, where hidden clinical patterns can be uncovered through automated text mining.(43)

Similarly, data-driven methods have been employed to predict adverse drug events before they manifest in patients. By combining prescription records with genetic and clinical information, researchers were able to anticipate unexpected drug–drug interactions, opening new perspectives for medication safety. These findings echoed earlier studies in Japanese databases, which showed that pharmacogenomic variants could be linked to adverse event frequencies in real-world prescribing.

AI has also been transformative in toxicology prediction, a critical step in drug development. Computational toxicology models, trained on structural and biochemical datasets, have achieved accuracy that surpasses traditional rule-based systems. Deep neural networks now enable early prediction of drug-induced liver injury and cardiotoxicity, reducing costly late-stage failures in pharmaceutical pipelines (44). More recently, integrative models combining transcriptomic responses and machine learning have demonstrated superior performance in forecasting compound-specific toxicities.

Together, these advances illustrate how AI not only supports clinical pharmacology but also bridges preclinical and clinical domains, creating a continuum from toxicology to bedside application. The harmonization of these approaches within regulatory frameworks will likely determine the speed at which they are embedded into pharmacological workflows.

2.4.2 Regulatory Frameworks

Regulatory agencies have acknowledged the importance of AI in pharmacology. The FDA’s database of AI/ML-enabled medical devices highlights growing acceptance, though challenges in validation remain (45). The UK’s MHRA AI roadmap (38) and the EU’s proposed Artificial Intelligence Act (46)aim to regulate AI systems according to risk levels.

In pharmacology, AI is increasingly visible in regulatory submissions for drug development, with trends showing growing integration from 2016 to 2021 (47). These initiatives underscore the dual responsibility of advancing innovation while safeguarding patient safety.

2.5 AI in Real-World Pharmacology and Medication Management

AI models are also integrated into real-world data (RWD) applications, bridging the gap between clinical trial efficacy and real-world effectiveness. RWD sources, including insurance claims, EHRs, and mobile health data, allow AI to monitor post-marketing drug outcomes, adherence, and polypharmacy risks (48).

Medication management is another domain of impact. AI-based clinical decision support systems have been deployed to reduce prescription errors and optimize therapy in chronic conditions such as anticoagulation and dialysis-related anemia (49,50). Systematic reviews indicate that AI-driven systems enhance patient safety and improve cost-effectiveness (51).

Figure 2: Traditional vs AI-Aided Drug Discovery: A Phase-by-Phase Comparison. (24)

Table 2: Applications of AI across pharmacological domains. (52)

3. ACHIEVEMENTS, CASE STUDIES, AND CURRENT APPLICATIONS OF AI IN PHARMACOLOGY

3.1 Successes in Drug Discovery and Repurposing

One of the most visible achievements of AI in pharmacology is its role in accelerating drug discovery. Traditional methods of drug design may take 10–15 years and cost billions of dollars, with a high attrition rate in late-stage trials. By contrast, AI platforms can generate candidate molecules in days, prioritize hits, and predict toxicity at early stages.

A landmark example is AlphaFold, which achieved unprecedented accuracy in protein structure prediction (26). By resolving protein conformations at near-experimental resolution, AlphaFold has transformed structure-based drug design, opening opportunities against previously inaccessible molecular targets.

Similarly, AI-driven drug repurposing provided rapid therapeutic options during the COVID-19 pandemic. The identification of baricitinib, a Janus kinase inhibitor, as a candidate treatment was facilitated by AI-based knowledge graph analysis, which highlighted its dual role in inflammation modulation and viral entry blockade (27). This case highlighted the speed and adaptability of AI, demonstrating how computational approaches can complement emergency pharmacological responses.

Other studies have shown that AI-based integrative frameworks can prioritize repurposing opportunities across Alzheimer’s disease, diabetes, and oncology (53). These approaches leverage transcriptomics, molecular networks, and drug–disease associations to discover hidden therapeutic potentials.

3.2 AI in Oncology and Personalized Medicine

Oncology has been at the forefront of AI integration. Machine learning has been employed to predict cancer susceptibility, treatment response, and prognosis. AI models trained on histopathology slides can distinguish subtle tumor features predictive of therapy outcomes (54). In breast cancer, AI systems analyzing pre-treatment tumor biopsies have successfully predicted pathological response to chemotherapy.

AI-enabled pharmacogenomics also contributes to oncology. For example, transfer learning has improved prediction of CYP2D6 haplotype functionality, enabling more precise dosing of tamoxifen and other CYP2D6-metabolized drugs (36). Beyond genetics, AI integrates multi-omics data to develop comprehensive tumor profiles guiding precision therapy.

Case studies extend to digital pathology and radiomics. Radiomic signatures derived from CT and MRI scans, analyzed via deep learning, have been used to annotate prognostic and molecular subtypes of ovarian and lung cancers (55). Such noninvasive biomarkers have potential to refine patient stratification in clinical trials.

The rise of chemoinformatics and deep learning has been a major driver of progress in drug discovery, complementing the achievements of knowledge graphs and protein structure prediction. Traditional QSAR models have now been extended through advanced neural networks, which capture non-linear and high-dimensional chemical features that were previously intractable. Similarly, transfer learning frameworks in chemistry have enabled accurate activity predictions even with limited training data, addressing a longstanding bottleneck in small-molecule discovery.

Beyond activity prediction, generative adversarial networks (GANs) have opened the door to de novo molecular design. For instance, GAN-based models have been trained to generate entirely new molecular scaffolds with desirable pharmacological properties. These approaches allow for accelerated virtual screening and prioritize candidates for wet-lab validation, cutting both costs and timelines.

At the same time, pharmacological AI has contributed to predictive toxicology in real-world contexts. Automated systems have been applied to extract drug–adverse event associations from biomedical literature and spontaneous reporting systems (15). More comprehensive reviews demonstrate that AI-driven toxicology tools can support both regulatory decision-making and clinical risk assessment (56). In addition, AI-enhanced meta-analyses of pharmacovigilance data have been shown to reduce signal detection errors compared with traditional methods (57).

Together, these advances illustrate that AI’s achievements are not limited to drug design or diagnostics but extend to the safety and sustainability of pharmacological interventions, strengthening the reliability of drug development pipelines.

3.3 Cardiovascular Pharmacology and Risk Prediction

AI has made significant progress in cardiovascular pharmacology. Large-scale studies have demonstrated that AI can predict myocardial infarction risk by integrating troponin levels with clinical variables, surpassing conventional scoring systems (58). In heart failure, machine learning models have been deployed to predict readmission and mortality, assisting clinicians in optimizing therapy (59).

Retinal fundus imaging has emerged as a surprising but powerful pharmacological biomarker source. AI models have predicted cardiovascular risk factors such as blood pressure, smoking status, and lipid levels directly from retinal images (60). Similarly, deep learning has detected anemia from fundus photographs, offering a non-invasive approach to pharmacological monitoring (61). These examples demonstrate how AI can link diagnostics to pharmacological interventions.

3.4 AI in Infectious Diseases and Pandemic Response

The COVID-19 pandemic provided a critical testbed for AI in pharmacology. Prediction models were rapidly developed to assess diagnosis, prognosis, and treatment outcomes. For example, an AI system deployed in emergency departments predicted deterioration risk among COVID-19 patients, enabling targeted pharmacological interventions (62).

Systematic reviews of AI models for COVID-19 prognosis revealed variability in quality, highlighting the need for standardized evaluation (13). Nevertheless, the pandemic demonstrated the potential of AI to accelerate drug repurposing, monitor real-world drug effects, and support adaptive trial designs under crisis conditions.

In infectious diseases beyond COVID-19, AI has been applied in antimicrobial stewardship, guiding precision dosing of antibiotics like vancomycin (63). These applications show how AI can help address antimicrobial resistance (AMR), a growing global pharmacological challenge.

3.5 AI in Neurology and Rare Diseases

AI applications extend to neurology, where drug discovery and patient monitoring are particularly challenging. In Parkinson’s disease, critical analyses have highlighted that while AI holds promise, its early evaluations have often been over-optimistic, necessitating more rigorous validation (3). Still, advances in wearable devices combined with AI signal processing allow continuous monitoring of pharmacological therapy responses, such as levodopa fluctuations.

For rare diseases, where conventional trial recruitment is difficult, AI-enabled synthetic control arms and predictive models facilitate trial feasibility. Digital biomarkers derived from imaging, electrophysiology, and omics data complement pharmacological research in this domain.

3.6 AI in Pharmacovigilance: Real-World Case Studies

Real-world integration of AI into pharmacovigilance systems has begun to yield results. In the UK, the MHRA implemented AI within its Yellow Card system to streamline adverse event detection and analysis, enhancing pharmacovigilance efficiency (38). In the US, FDA’s Sentinel Initiative has incorporated AI-driven analytics into safety surveillance.

Case studies show that EHR text mining identifies signals that would be missed in structured data. (64) demonstrated that free-text clinical notes could be mined with ML models to detect adverse drug reactions with high sensitivity. Additionally, social media surveillance systems, such as DeepADEMiner, have flagged real-world drug safety issues months before regulatory alerts.

These examples illustrate the shift from passive, retrospective safety monitoring to proactive, predictive pharmacovigilance supported by AI.

3.7 Clinical Decision Support and Medication Management

Clinical decision support (CDS) systems powered by AI are increasingly deployed in hospitals and pharmacies. In dialysis patients, AI-based CDS improved anemia management outcomes while optimizing erythropoietin dosing (50). Similarly, AI-guided anticoagulation management reduced the risk of nonadherence and thromboembolic events (49). Systematic reviews confirm that AI-based interventions enhance medication safety by reducing prescription errors, alert fatigue, and drug–drug interactions (51,65). These real-world achievements demonstrate the scalability of AI across diverse pharmacological contexts.

Figure 3: Core Applications of Artificial Intelligence in Pharmacology and Pharmaceutical Sciences.

Table 3: Core achievements and applications of AI in pharmacology

4.1 Technical Challenges

4.1.1 Data Quality and Availability

AI in pharmacology depends on access to large, diverse, and high-quality datasets. However, biomedical and pharmacological data are often fragmented, incomplete, biased, or unstandardized. Electronic health records (EHRs) frequently contain missing or inconsistent values, which can mislead algorithms unless carefully addressed through imputation or robust model design (10,66). Furthermore, pharmacovigilance databases such as the FDA’s FAERS suffer from under-reporting and lack of denominator data, limiting their utility without careful preprocessing.

4.1.2 Hidden Stratification and Dataset Shift

Hidden stratification occurs when subgroups within datasets are not adequately represented or labeled, leading to models that perform well in aggregate but fail in specific subpopulations. This issue is particularly critical in pharmacology, where genetic variability, comorbidities, and age can strongly influence drug response (67). Dataset shift, in which the distribution of data changes between training and deployment environments, also undermines reliability (68)

4.1.3 Reproducibility and Transparency

Many AI models in pharmacology remain black-box systems, raising concerns about reproducibility and interpretability. Studies often report high accuracy on internal datasets but fail when tested externally. Without transparent methodologies, including open-source code, clear reporting (CONSORT-AI, SPIRIT-AI), and independent validation, translation into real-world pharmacology remains uncertain (16,17).

4.1.4 Explainability

Explainable AI (XAI) is essential for clinical adoption. Pharmacologists and clinicians need to understand why a model makes a recommendation, especially when it involves critical decisions like drug dosing or safety alerts. Techniques such as saliency maps and attention mechanisms provide some interpretability, but remain imperfect (69). The gap between model performance and explainability remains a persistent barrier.

A further set of challenges relates to the safety and reliability of pharmacological AI systems when deployed in real-world practice. Early investigations demonstrated that even well-calibrated machine learning models can fail to generalize across populations, leading to unexpected clinical outcomes. More recent work using large-scale pharmacovigilance databases has highlighted how machine learning can both improve and complicate adverse drug reaction detection, depending on data quality and feature representation.

Another major barrier lies in the under-reporting of adverse drug reactions (ADRs), a well-documented problem that limits the robustness of any AI model trained on pharmacovigilance data. Without strong reporting incentives, algorithms risk perpetuating existing blind spots in drug safety.

To address this, integrative data resources have been developed that link chemical, biological, and clinical domains. Databases such as DrugBank and STITCH offer curated connections between compounds, targets, and ADRs, serving as valuable foundations for AI-based pharmacological models. However, concerns remain regarding data standardization and the reproducibility of findings across platforms.

Finally, in low- and middle-income countries, AI pharmacovigilance must confront infrastructural and regulatory hurdles. Studies emphasize the importance of local adaptation, as systems trained on Western data may misrepresent risks in Asian or African contexts. Unless datasets are inclusive and context-aware, AI will risk reinforcing global health inequities rather than alleviating them.

4.2 Ethical, Legal, and Social Challenges

4.2.1 Algorithmic Bias

Bias in AI systems arises from unrepresentative datasets or flawed design. In pharmacology, biased models can amplify health inequities, leading to inappropriate dosing, under-detection of adverse events, or exclusion of vulnerable populations (70,71). For example, if genomic datasets underrepresent minority populations, pharmacogenomic predictions may be skewed.

4.2.2 Accountability and Liability

Determining legal responsibility for AI-driven pharmacological decisions remains unresolved. If an AI-based dosing system leads to an adverse event, should liability rest with the clinician, the hospital, the software vendor, or the regulator? Legal scholars emphasize the need for clear liability frameworks before AI can be safely embedded in pharmacology (72).

4.2.3 Privacy and Security

Pharmacological AI often requires access to sensitive patient-level data. This raises issues of data protection, consent, and cybersecurity. Breaches of medical AI systems could expose genetic or prescription data, eroding trust in pharmacology research. Emerging solutions include federated learning (training models across distributed datasets without centralizing data), though implementation remains complex.

4.3 Systemic and Regulatory Challenges

4.3.1 Regulatory Hesitancy

Regulators are still adapting to AI’s unique challenges. Unlike conventional medical devices or drugs, AI algorithms may evolve over time (continuous learning systems), making static regulatory approval insufficient (45,73). While agencies like the FDA and EMA have begun issuing guidance, frameworks remain fragmented, especially for pharmacological applications such as adaptive dosing or trial optimization.

4.3.2 Lack of Standardized Frameworks

Although reporting standards such as SPIRIT-AI and CONSORT-AI exist, their adoption in pharmacology research is inconsistent (16,17). Without mandatory compliance, reproducibility and trust remain limited.

4.3.3 Cost-Effectiveness and Scalability

AI solutions may be costly to develop and deploy. Smaller pharmaceutical companies and public-sector pharmacology departments may struggle to access the computational infrastructure required for advanced AI. Even when effective, AI systems must be scaled and integrated into existing workflows, which is often more complex than initial pilot studies suggest.

4.4 Societal Concerns and Clinical Trust

4.4.1 Human–AI Collaboration

Clinicians and pharmacologists may be reluctant to trust AI-generated recommendations, particularly when they lack transparency or appear to contradict clinical experience. Studies reveal that trust is built when AI tools are framed as assistive rather than replacement technologies (19). This concept of “augmented intelligence” emphasizes human oversight, maintaining clinical responsibility while benefiting from AI insights.

4.4.2 Risk of Widening Inequalities

AI has the potential to worsen global inequities in pharmacology. High-income countries with advanced computational infrastructure may accelerate AI-driven discoveries, while low- and middle-income countries (LMICs) risk being left behind. Moreover, datasets predominantly sourced from Western populations may not generalize globally, further exacerbating disparities (74).

4.5 Case Studies Illustrating Limitations

• COVID-19 Prediction Models: Early AI models predicting COVID-19 prognosis performed poorly when externally validated, reflecting overfitting and lack of generalizability (13).
• Hidden Bias in Imaging: Models trained on imaging datasets have failed in real-world deployments due to hidden stratification, leading to clinically meaningful errors (67).
• Drug Safety Monitoring: Despite advances, pharmacovigilance systems still suffer from false positives when mining social media data, requiring careful human oversight (56).

These examples emphasize that while AI shows promise, it must be rigorously validated and integrated cautiously.

4.6 Potential Solutions to Current Challenges

Efforts are underway to address these limitations. Federated learning and privacy-preserving AI may reduce data-sharing risks. Explainable AI techniques are advancing toward more interpretable decision support. Regulatory sandboxes, where AI tools are tested in controlled environments, offer a safe space for iterative improvement. Finally, cross-disciplinary education of pharmacologists, clinicians, and data scientists can improve mutual understanding and trust.

Table 4: Core challenges and pragmatic mitigations in AI-driven pharmacology

5.1 Blockchain and AI in Pharmacology

One of the most promising intersections is the combination of blockchain technology and AI. Pharmacology depends on secure, transparent, and traceable data, particularly in clinical trials, drug supply chains, and pharmacovigilance. Blockchain, with its decentralized and tamper-proof ledger, can ensure integrity and transparency, while AI provides the analytic power to interpret and act on these data streams.

For instance, clinical trial registries on blockchain could prevent selective reporting or data manipulation, ensuring accountability across sponsors and regulators (75). When integrated with AI, such systems could automatically analyze trial data, flag anomalies, and optimize trial endpoints. Similarly, blockchain-secured supply chains can prevent counterfeit drugs, a major pharmacological and public health threat in low- and middle-income countries.

In pharmacovigilance, blockchain-enabled patient reporting systems could empower individuals to report adverse events directly, with AI aggregating and analyzing these inputs in real time. This dual system could revolutionize post-marketing surveillance by enhancing both security and efficiency.

Figure 5: Integration of Blockchain and AI in Drug Discovery & Healthcare Systems. Made with Mermaid Website