A Review: Role Of Artificial Intelligence in Drug Discovery: A Comprehensive Review of Emerging Trends in Pharmacology

Drug discovery is the process of identifying and developing new therapeutic agents for the treatment of diseases. It is a highly complex, time-consuming, and resource-intensive process, typically requiring 10–15 years and substantial financial investment for the development of a single drug. Prior to clinical evaluation, candidate molecules must undergo extensive preclinical testing in cell-based systems and animal models to ensure their safety and efficacy. Despite continuous advancements in biomedical research, the success rate of drug development remains low, with only a small proportion of candidates ultimately achieving regulatory approval. The overall cost of drug development is estimated to exceed $2.5 billion, highlighting the urgent need for more efficient and cost-effective strategies.^[1]                                                         

Traditionally, drug discovery begins with the identification of a suitable biological target involved in disease progression, followed by the screening of large chemical libraries to identify potential lead compounds. However, this hypothesis-driven approach is often labor-intensive and associated with high attrition rates. In this context, artificial intelligence (AI) has emerged as a promising tool to address the limitations of conventional drug discovery methods. AI technologies, including machine learning, deep learning, and natural language processing, enable rapid analysis of large-scale biological and chemical datasets, thereby improving target identification, accelerating compound screening, and facilitating the design of safer and more effective therapeutic agents.^[2] Furthermore, AI has become an integral component of systems pharmacology and drug–target interaction prediction, contributing significantly to modern drug development strategies. By leveraging advanced computational models, AI enhances the prediction of molecular interactions and supports the identification of novel therapeutic targets. In clinical research, AI facilitates improved patient selection, real-time monitoring of treatment responses, and adaptive modifications of clinical protocols, thereby increasing the efficiency and success rates of clinical trials.^[3]Artificial intelligence also plays a critical role in enhancing the precision and efficiency of clinical trials. It enables accurate patient stratification based on clinical and biological characteristics and predicts individual responses to therapeutic interventions. The integration of real-world evidence and large-scale data analytics further supports efficient patient recruitment, reduces operational costs, and improves overall trial outcomes.^[4] With ongoing technological advancements, AI is increasingly influencing drug development by addressing the limitations of traditional approaches, accelerating therapeutic discovery, and improving patient care. However, challenges such as data quality, model interpretability, ethical considerations, and regulatory compliance must be carefully addressed to ensure its safe and effective implementation^.[5] Fundamental AI techniques, including neural networks for predictive modeling and reinforcement learning for drug design, have become essential tools in computational pharmacology. These approaches enable accurate prediction of drug behavior and facilitate the rapid design and optimization of novel compounds, thereby enhancing the efficiency and precision of the drug discovery process.^[6] Machine learning has significantly enhanced the process of lead optimization in drug development by enabling the early prediction of critical pharmacokinetic and safety parameters, including absorption, distribution, metabolism, excretion, and toxicity (ADMET). These predictive capabilities facilitate the identification of promising drug candidates at an early stage, thereby reducing the risk of late-stage failure and improving overall development efficiency.^[7] In addition, artificial intelligence (AI) has demonstrated considerable potential in drug repurposing, which involves identifying new therapeutic applications for existing drugs. This strategy offers a cost-effective and time-efficient alternative to conventional drug development approaches. Notably, during the COVID-19 pandemic, AI-based tools were rapidly employed to identify potential therapeutic agents, underscoring the practical utility of AI in addressing urgent global health challenges.^[8]

Artificial intelligence is transforming the drug discovery process by improving speed, cost-effectiveness, and success rates. Traditional drug discovery methods are often labor-intensive, time-consuming, and expensive, whereas AI-driven approaches leverage large-scale datasets and advanced computational algorithms to accelerate each stage of the drug development pipeline. These technologies enable efficient identification of potential drug candidates, accurate prediction of drug–target interactions, and rational design of optimized chemical structures with enhanced precision.^[9,10] Machine learning (ML) and deep learning (DL) have become integral components of modern drug discovery. These approaches utilize predictive modeling and data-driven insights to support informed decision-making and enhance research outcomes. By analyzing complex biological and chemical datasets, ML and DL techniques can uncover hidden patterns and relationships that are difficult to detect through conventional methods. In particular, machine learning is widely applied in predicting drug–protein interactions, assessing toxicity risks, and optimizing molecular properties, thereby contributing to the development of safer and more effective therapeutic agents.^[11]

1. Artificial Intelligence in Drug Discovery

Artificial intelligence (AI) is rapidly evolving and is anticipated to remain a significant driving force in the field of drug discovery ^[12]. By leveraging advanced computational approaches such as machine learning (ML), deep learning (DL), and AI-driven screening techniques, researchers can effectively address many limitations associated with traditional drug discovery methods. These advanced methodologies facilitate the rapid development of safer and more efficacious therapeutic agents. Furthermore, the integration of AI into drug discovery is accelerating the advancement of precision medicine, wherein data-driven strategies support innovation in healthcare and enable the development of targeted therapeutic interventions ^[13]. The identification of disease-associated biological targets represents a critical initial step in the drug discovery process. Artificial intelligence (AI)-based models enable the analysis of large-scale datasets, including genomic, proteomic, and clinical information, to identify potential drug targets with enhanced accuracy. These computational approaches can predict protein structures, molecular interactions, and functional roles, thereby reducing reliance on traditional trial-and-error experimental methods ^[14].

2. AI in Target Identification

The identification of disease-associated biological targets is a fundamental step in drug discovery. Artificial intelligence facilitates this process by enabling the analysis of complex biological datasets and improving the accuracy of target identification. AI-based models integrate genomic, proteomic, and clinical data to identify potential therapeutic targets and provide insights into disease mechanisms ^[14]

2.1 Role of AI in Target Identification

Artificial intelligence introduces an advanced and data-driven approach to drug target identification by integrating complex biological datasets with predictive computational algorithms. Machine learning models trained on multi-omics data—including genomics, proteomics, transcriptomics, and metabolomics—enable the identification of potential therapeutic targets at the molecular level with improved accuracy and efficiency ^[14].

In contrast to traditional hypothesis-driven approaches, AI-based methodologies adopt a hypothesis-independent framework, allowing the discovery of hidden patterns and complex relationships within large-scale datasets. For instance, supervised learning algorithms can be employed to predict gene–disease associations through the analysis of genome-wide association studies (GWAS) and transcriptomic data, thereby facilitating more precise identification of potential therapeutic targets ^[15].

Unsupervised learning algorithms, including clustering techniques, can group genes and proteins based on shared functional characteristics, thereby aiding in the identification of novel druggable pathways. Additionally, natural language processing (NLP) methods enable the systematic analysis of vast volumes of scientific literature, allowing extraction of relevant information on emerging drug targets and supporting researchers in staying updated with recent advancements in the field ^[15].

2.2 Case Studies in AI-Driven Target Identification

AlphaFold and Protein Structure Prediction

The development of DeepMind’s AlphaFold has significantly advanced the field of structural biology by enabling highly accurate prediction of protein three-dimensional structures. This achievement addresses a longstanding challenge in drug discovery by facilitating the identification of potential binding sites and enabling the rational design of therapeutic agents. It is particularly valuable for targeting proteins associated with complex diseases, including neurodegenerative disorders ^[16].

CRISPR and AI Integration

The integration of artificial intelligence with CRISPR technology has enhanced the precision of identifying genetic vulnerabilities in cancer cells. AI-guided CRISPR screening approaches enable the systematic analysis of gene knockout data to determine genes essential for cancer cell survival. This combined strategy provides valuable insights for the development of novel and targeted therapeutic interventions ^[17].

2.3 AI in Rare Disease Target Discovery

Artificial intelligence (AI) has demonstrated considerable potential in the identification of therapeutic targets for rare diseases through the analysis of genomic and clinical datasets. Machine learning models, in particular, have facilitated the discovery of biomarkers and molecular targets in conditions such as lysosomal storage disorders. These advancements have contributed significantly to the development of targeted therapeutic strategies for such diseases ^[18].

2.4 Validation through Predictive Models

Following the identification of a potential therapeutic target, rigorous validation is essential to confirm its biological relevance and clinical applicability. Artificial intelligence supports this process through advanced computational frameworks, such as graph neural networks (GNNs),

which model complex biological interactions and predict the effects of modulating specific protein targets within cellular systems ^[19].

These computational approaches are capable of evaluating interactions between small molecules and protein targets, as well as their downstream effects on cellular pathways. Deep learning architectures, particularly convolutional neural networks (CNNs), are highly effective in predicting drug–target interactions. By analyzing protein structures and estimating binding affinities, these models assist researchers in determining the likelihood of success for potential drug candidates ^[20].

3. AI in Lead Optimization and Drug Design

3.1 Lead Optimization

Lead optimization is a critical stage in drug development that involves refining chemical compounds to enhance their efficacy, safety, and selectivity. Conventional approaches rely on iterative cycles of chemical synthesis and biological evaluation, which are both time-intensive and costly. Moreover, these methods frequently encounter challenges such as off-target effects and limited bioavailability, contributing to high attrition rates during preclinical and clinical development ^[21].

3.2 De Novo Drug Design with AI

One of the most promising applications of artificial intelligence in drug discovery is de novo drug design, wherein novel molecular structures are generated that have not been previously synthesized. Unlike conventional approaches that rely on existing chemical libraries, AI-driven methodologies enable the design of new compounds tailored to specific biological and physicochemical properties.

Reinforcement learning (RL) is commonly employed in de novo drug design to iteratively optimize molecular structures by incorporating feedback on parameters such as binding affinity, stability, and molecular conformation. Additionally, generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), are capable of generating new chemical entities by learning patterns from existing datasets. These approaches facilitate the expansion of chemical libraries and enable exploration of previously uncharted chemical spaces.

Despite these advantages, certain limitations persist. AI-generated molecules may not always exhibit adequate stability, synthetic feasibility, or biological activity, which can limit their practical applicability in drug development ^[22].

4. AI in Virtual Screening

Virtual screening represents a crucial stage in the drug discovery process, involving the use of computational tools to evaluate extensive libraries of chemical compounds. This approach enables the identification of molecules with a high likelihood of interacting effectively with a specific biological target ^[24].

Structure-based virtual screening (SBVS) utilizes the three-dimensional structure of a target protein to predict the binding potential of candidate compounds within its active site ^[25]. Traditionally, this method relies on molecular docking simulations, in which multiple ligand conformations are generated and evaluated based on estimated binding energies. However, experimental validation remains essential, as conventional computer-aided drug design (CADD) approaches may not always achieve optimal predictive accuracy.

In contrast, ligand-based virtual screening (LBVS) relies on existing knowledge of compounds known to interact with a specific target. Machine learning models analyze physicochemical and structural properties (descriptors) of these ligands to identify new candidates with similar characteristics. This approach is commonly implemented through quantitative structure–activity relationship (QSAR) modeling, which has been widely used in drug discovery.

Recent advancements in artificial intelligence have significantly enhanced QSAR methodologies. Deep learning techniques now enable efficient screening of extremely large chemical libraries and are often integrated with other virtual screening strategies such as pharmacophore modeling and molecular docking. In SBVS, AI contributes to improved classification algorithms, more accurate binding site identification, and enhanced scoring functions for ligand–target interactions.

The development of advanced scoring functions has become a major focus of research, as these tools support lead optimization, ADMET prediction, and refinement of QSAR models. Machine learning-based scoring approaches have demonstrated improved performance compared to traditional methods, while deep learning models—particularly CNNs—are increasingly adopted in virtual screening due to their ability to identify complex structural patterns associated with successful binding interactions.

As the availability of high-quality experimental data continues to expand, deep learning-based scoring functions are expected to complement and potentially replace conventional machine learning approaches in virtual screening applications ^[23].

5. AI in Preclinical and Clinical Research

Artificial intelligence is significantly transforming both preclinical and clinical research by improving efficiency, reducing development timelines, and minimizing the risk of failure. Conventional drug development processes typically involve extensive experimental studies, costly clinical trials, and high attrition rates, often due to unforeseen toxicity or inadequate therapeutic efficacy ^[27].

Drug discovery generally begins with a large pool of chemical compounds, including potential lead candidates. As these compounds progress through the development pipeline, rigorous selection and optimization processes are applied to identify those with the highest therapeutic potential, ultimately resulting in the approval of a single drug. Artificial intelligence contributes significantly during the early stages by predicting the likelihood of compound failure, particularly due to unfavorable absorption, distribution, metabolism, and excretion (ADME) properties or insufficient target binding affinity ^[26].

Early identification of potential failures helps prevent costly setbacks in later stages of drug development. In clinical trials, AI plays a critical role by analyzing large-scale patient datasets, identifying suitable participants, and predicting treatment outcomes, thereby improving efficiency and success rates ^[28].

Machine learning models enable patient stratification based on clinical, demographic, and genetic characteristics, allowing trials to focus on individuals most likely to respond to a given therapy. Furthermore, predictive modeling, data-driven safety evaluation, and AI-assisted optimization of trial design enhance overall efficiency, reduce resource utilization, and accelerate the delivery of effective treatments to patients ^[29]. As AI continues to evolve, it is expected to further advance precision medicine and improve patient outcomes globally ^[30].

6. AI in Preclinical Testing and Safety Assessment

Artificial intelligence has significantly transformed preclinical testing and safety assessment through several important applications:

1. Prediction of Toxicity and Adverse Effects
AI algorithms utilize large-scale datasets to predict potential toxicities and adverse effects associated with drug candidates. Early identification of such risks enables prioritization of safer compounds, thereby reducing development time and minimizing late-stage failures.

2. Optimization of Animal Model Selection
AI assists in selecting appropriate animal models by analyzing biological and genetic similarities between species. This improves translational accuracy to human systems and enhances the reliability of preclinical findings.

3. Advanced Data Analysis
AI-based tools enable efficient processing and analysis of extensive datasets generated during preclinical studies. These tools identify complex patterns, correlations, and safety concerns, supporting improved data interpretation and decision-making.

4. Early Safety Assessment
AI facilitates early evaluation of safety profiles by analyzing multiple biological parameters. This approach helps identify and mitigate risks at an early stage, ensuring patient safety and supporting regulatory compliance.

5. Reduction of Animal Testing
AI-driven in silico simulations can replicate complex biological processes, reducing reliance on animal experimentation. These models predict drug interactions, efficacy, and safety, offering a cost-effective and ethically responsible alternative in drug development ^[31].

Applications of Artificial Intelligence in Preclinical Development

The preclinical development phase serves as a critical bridge between early-stage research and clinical trials, ultimately supporting the submission of an Investigational New Drug (IND) application [32]. This stage involves the systematic evaluation of drug candidates through in vitro (cell-based), in vivo (animal-based), and in silico (computational) studies to assess their safety and efficacy prior to human testing. Increasingly, artificial intelligence (AI) is being integrated into this phase to enhance testing efficiency, reduce development costs, and facilitate informed decision-making. By enabling rapid data analysis and predictive modeling, AI significantly improves the reliability and speed of preclinical evaluations.

Artificial Intelligence in Clinical Trial Optimization

Clinical trials represent a crucial phase in drug development; however, they are often associated with challenges such as slow patient recruitment, inefficient study protocols, and unforeseen complications. Artificial intelligence (AI) is increasingly transforming clinical trial processes by enhancing operational efficiency, improving accuracy, and optimizing patient outcomes.

Patient Recruitment and Selection Using AI

Traditional patient recruitment methods rely heavily on manual evaluation of medical records, making the process time-consuming and susceptible to errors. Artificial intelligence overcomes these limitations by analyzing large-scale datasets to identify eligible participants more efficiently. Natural language processing (NLP) techniques enable the extraction of relevant information from electronic health records, including demographic data, medical history, and genetic profiles. Consequently, AI-driven recruitment accelerates trial initiation and increases the likelihood of achieving enrollment targets.

Optimization of Clinical Trial Protocols

Artificial intelligence plays a pivotal role in refining clinical trial design. Machine learning algorithms analyze historical and real-world datasets to identify patterns that inform improvements in study protocols, including dosing strategies, endpoint selection, and inclusion or exclusion criteria. These data-driven optimizations enhance trial efficiency, reduce costs, and increase the probability of successful clinical outcomes.

Real-Time Monitoring and Data Analysis

Conventional clinical trials typically rely on periodic data collection and manual monitoring, which may delay the detection of adverse events or unexpected patient responses. In contrast, AI enables continuous, real-time monitoring by analyzing incoming data streams and identifying deviations from expected patterns. This capability enhances patient safety and allows timely, evidence-based decision-making during clinical trials.

Prediction of Patient Responses

Artificial intelligence enables the prediction of individual patient responses to therapeutic interventions by integrating genetic, biomarker, and clinical data. This facilitates effective patient stratification into subgroups, ensuring that treatments are administered to individuals most likely to benefit. Such targeted approaches improve clinical trial success rates and support the advancement of personalized medicine.

Enhancement of Trial Efficiency and Outcomes

The integration of AI across clinical trial processes significantly enhances overall efficiency and outcomes. By accelerating recruitment, optimizing study design, enabling real-time monitoring, and supporting personalized treatment strategies, AI contributes to faster trial completion and reduced development costs. Furthermore, these advancements promote a patient-centric approach, ultimately improving clinical success rates and healthcare outcomes ^[31].

Artificial Intelligence in Drug Repurposing and Clinical Trials

Drug repurposing, also known as drug repositioning, involves identifying new therapeutic applications for existing drugs and represents a cost-effective and time-efficient alternative to traditional drug development. Historically, this process relied on serendipitous discoveries and labor-intensive investigations, limiting its scalability ^[33].

The integration of artificial intelligence has significantly advanced drug repurposing by enabling the analysis of large-scale molecular, clinical, and pharmacological datasets. Machine learning (ML) and natural language processing (NLP) techniques facilitate the identification of previously unrecognized relationships between drugs and disease pathways, thereby enhancing the efficiency and accuracy of repurposing strategies ^[34].

Several notable examples highlight the impact of AI-driven drug repurposing. During the COVID-19 pandemic, AI-based approaches identified approved drugs with potential antiviral activity. For instance, Baricitinib was rapidly advanced into clinical trials and subsequently authorized for emergency use ^[30]. Similarly, Remdesivir was predicted to have antiviral activity against SARS-CoV-2 and later validated in clinical studies ^[37]. In oncology, Thalidomide was successfully repurposed for the treatment of multiple myeloma ^[35].

Overall, AI-driven drug repurposing enhances the therapeutic potential of existing drugs, reduces development costs, and accelerates the availability of new treatment options. As AI technologies continue to evolve, their role in addressing unmet clinical needs is expected to expand further [36].

Clinical Trials and Real-World Evidence

Following preclinical development, drug candidates progress to the clinical trial phase, which is significantly more resource-intensive and costly. A major challenge at this stage is patient recruitment and selection, as inadequate enrollment can lead to delays or termination of clinical studies.

Artificial intelligence enhances recruitment by analyzing large-scale electronic health record (EHR) datasets to identify eligible patients more efficiently. This reduces both time and financial burdens associated with enrollment. Additionally, AI enables continuous patient monitoring during clinical trials by integrating EHR data with real-time inputs from wearable devices, such as smartwatches. This facilitates early detection of adverse effects and atypical responses, thereby improving safety and trial management.

Furthermore, AI plays a crucial role in generating real-world evidence by extracting and analyzing data from healthcare databases and transforming it into clinically meaningful insights ^[38,39]. An emerging application in this domain is the concept of digital twins. A digital twin is a virtual representation of an individual patient that reflects physiological and clinical characteristics. By utilizing patient-specific data, digital twins can simulate disease progression and predict responses to therapeutic interventions, thereby supporting personalized medicine and informed clinical decision-making ^[40].

Fig .Artificial Intelligence in real world applications and their providers ^[47]

How Artificial Intelligence Can Be Used in Clinical Pharmacology

Artificial intelligence (AI) has the potential to transform multiple domains of clinical pharmacology, extending beyond diagnostics and biomarker development. It is increasingly being applied across the drug development and healthcare continuum to improve efficiency, safety, and therapeutic outcomes. Key areas of impact include drug discovery and development, clinical trials, medicines management, personalized medicine and precision dosing, pharmacogenomics, pharmacovigilance, adverse drug reaction (ADR) monitoring, and clinical toxicology. The application of AI in preclinical pharmacology has been discussed in earlier sections.

Diagnosis and Prognostication

One of the most significant advancements of artificial intelligence in healthcare is its application in diagnostic support and medical imaging analysis. A substantial proportion of AI-based algorithms approved by the U.S. Food and Drug Administration (FDA) are designed for diagnostic purposes.

Applications of AI in diagnosis include the detection of cardiac arrhythmias using wearable devices such as smartwatches, image preprocessing and enhancement in radiological imaging, and real-time identification of colonic mucosal lesions during endoscopic procedures. The success of AI in medical imaging is largely attributed to advancements in computer vision techniques, particularly convolutional neural networks (CNNs), along with the availability of large-scale anonymized datasets, including chest X-rays, retinal images, and dermatological image databases.

In addition to diagnosis, AI plays a crucial role in prognostication and risk stratification. Machine learning algorithms can model complex, nonlinear relationships within clinical datasets to predict patient outcomes. For example, decision tree-based models have been used to stratify risk in patients with acute coronary syndrome by integrating clinical parameters and high-sensitivity troponin levels, demonstrating the potential of AI in predictive clinical decision-making ^[41].

Clinical Toxicology

Applications of artificial intelligence in clinical toxicology remain relatively limited compared to other areas of clinical pharmacology. A rule-based AI model has been developed to identify poisoned patients and classify them into major toxidromes, including anticholinergic, cholinergic, opioid, sedative-hypnotic, serotonin toxicity, and sympathomimetic syndromes. Although this model demonstrated improved performance compared to traditional decision tree algorithms, it did not achieve the diagnostic accuracy of experienced clinicians ^[42]. These findings highlight both the potential and current limitations of AI in toxicological assessment.

Pharmacovigilance

Pharmacovigilance involves the monitoring, detection, assessment, and prevention of adverse effects associated with drugs and vaccines. While clinical trials establish initial drug safety, certain adverse reactions may only become evident during long-term use or in patients with comorbidities ^[43].

Artificial intelligence is increasingly being applied in pharmacovigilance through AI-driven pharmacovigilance (AIPV) systems for the detection of adverse drug reactions and medication errors. These systems integrate and analyze data from multiple sources, including electronic health records, spontaneous reporting systems, and real-world datasets. This enables a proactive and continuous approach to safety monitoring, ultimately improving drug safety profiling and patient outcomes ^[44,45].

Pharmacogenomics

Pharmacogenomics is increasingly integrated into clinical practice to support individualized therapy. For instance, the cytochrome P450 enzyme CYP2D6 is responsible for the metabolism of approximately 20% of clinically used drugs. Artificial intelligence enhances the identification and interpretation of genetic variations that influence drug metabolism and response.

Neural network models have demonstrated improved accuracy in predicting CYP2D6-mediated metabolism of drugs such as tamoxifen in patients with breast cancer. Additionally, convolutional neural networks trained on both synthetic and real-world datasets have shown reliable prediction of CYP2D6 haplotype function [46]. These advancements support the development of personalized therapeutic strategies and precision medicine.

Machine Learning

Machine learning (ML) is a branch of artificial intelligence that enables systems to analyze data, identify patterns, and make decisions with minimal human intervention. ML algorithms continuously improve their performance as they are exposed to more data.

Machine learning can be broadly classified into three types: supervised learning, where models are trained using labeled datasets; unsupervised learning, where hidden patterns are identified in unlabeled data; and reinforcement learning, where models learn through interaction with an environment by maximizing cumulative rewards.

The development of machine learning models typically involves several key steps, including data preprocessing (cleaning datasets, handling missing values, and removing outliers), feature selection and extraction, selection of appropriate algorithms, model training, and performance evaluation using suitable validation metrics.

Deep Learning

Deep learning (DL) is a specialized subset of machine learning that utilizes artificial neural networks with multiple hidden layers to learn from large volumes of unstructured or unlabeled data. These models progressively extract higher-level features from raw input data, making them highly effective for complex and high-dimensional datasets.

Despite their advantages, deep learning models require substantial computational resources and are often considered “black box” systems due to their limited interpretability. Large language models (LLMs), such as ChatGPT and GPT-4, represent advanced deep learning architectures capable of generating human-like text and supporting various biomedical applications.

Artificial Intelligence in Drug Discovery

Machine learning and deep learning, as key components of artificial intelligence, are increasingly utilized to address major challenges in drug discovery. The drug development process is inherently complex, costly, and time-consuming, involving multiple stages from target identification to clinical evaluation. AI technologies streamline these processes by improving speed, enhancing efficiency, and increasing predictive accuracy. As a result, AI-driven approaches significantly contribute to the development of safer and more effective therapeutic agents ^[48].

Fig.Machine learning and deep learning are subsets of artificial intelligence.

Fig. Comparative analysis: Deep learning mechanisms and human brain functions.

Applications of AI in Pharmacology

Artificial intelligence (AI), through techniques such as machine learning (ML), deep learning (DL), including convolutional neural networks (CNNs), and natural language processing (NLP), has significantly transformed the field of pharmacology and influenced nearly all stages of drug development and clinical application. AI plays a crucial role in drug discovery by enabling the identification of novel drug candidates and therapeutic targets through computational modeling and predictive analytics. In clinical trials, AI contributes to the optimization of study design, patient recruitment and selection, and real-time monitoring of trial outcomes. Furthermore, AI supports post-marketing surveillance by facilitating continuous monitoring of the safety and effectiveness of approved drugs in real-world populations. In addition, AI advances precision medicine by enabling the development of individualized treatment strategies based on patient-specific characteristics such as genetics, clinical history, and biomarker profiles^.[49]

Fig. Applications of artificial intelligence in pharmacological research and precision medicine. Artificial intelligence, utilizing machine learning, deep learning techniques (including convolutional neural networks, CNNs), and natural language processing (NLP), has transformed drug discovery and development. This includes the drug discovery phase, clinical trial phase, and post-marketing surveillance, as well as advancements in precision medicine. CNN: convolutional neural networks. The figure is created by the authors of this study.