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SSP Modern Pharmacy and Medicine (ISSN 2733-368X), Volume 5 Issue 2, Apr-Jun 2025
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Prospects for the Use of Artificial Intelligence in Personalized Medicine,
Pharmaceutical Design and Education
Viktoria Dovzhuk (Doctor of Pedagogical Sciences,
Candidate of Pharmaceutical Sciences, Associate Professor,
Bogomolets National Medical University, Ukraine)
Liudmyla Konovalova (Candidate of Pedagogical
Sciences, Associate Professor, Bogomolets National Medical
University, Ukraine)
Natela Dovzhuk (Candidate of Pedagogical Sciences,
Associate Professor, Bogomolets National Medical University,
Ukraine)
Serhii Konovalov (Candidate of Medical Sciences,
Associate Professor, Bogomolets National Medical University,
Ukraine)
Liudmyla Konoshevych (Candidate of Pharmaceutical
Sciences, Associate Professor, Bogomolets National Medical
University, Ukraine)
Nataliia Motorna (Candidate of Biological Sciences,
Bogomolets National Medical University, Ukraine)
Corresponding author: Viktoria Dovzhuk
Received: April 02, 2025
Published: April 17, 2025
Abstract. The article reviews modern approaches
to the use of artificial intelligence in personalized
medicine, pharmaceutical design and education, in
particular in the pharmaceutical industry, in the
processes of search, development, new drugs,
personalization of pharmacotherapy and in the
education of specialists in these processes. An
analysis of the main areas of application of
artificial intelligence, its advantages and
challenges, as well as the impact on pharmaceutical
design in the creation of new drugs is carried out.
Special attention is paid to the role of artificial
intelligence in personalized medicine, prediction of
clinical and pharmacological properties,
optimization of clinical trials. Ethical and
regulatory aspects of integrating artificial
intelligence into medical and pharmaceutical
education are considered. Prospects for further
development and improvement of the
implementation of artificial intelligence in
medicine, pharmaceutical design and education are
identified. The potential for artificial intelligence to
accelerate drug discovery, reduce costs, and
enhance treatment precision through real-time
analysis of vast datasets is particularly highlighted.
Additionally, educational curricula incorporating
artificial intelligence-based simulations and virtual
reality tools for training future pharmaceutical
professionals are explored.
Keywords: artificial intelligence, pharmaceutical
design, development of new drugs, personalized
medicine, clinical trials, pharmacotherapy, ethical
aspects, regulatory requirements.
Introduction. Today, against the backdrop of Covid, post-Covid, and long-Covid health
disorders, the issues of searching for, developing new drugs, quality control, diagnostic methods,
treatment, pharmacotherapy regimens, and training specialists to perform these procedures remain
relevant [1-10].
Among the latest digital technologies, artificial intelligence technologies are rapidly
developing. Artificial intelligence (AI) is one of the most promising technologies of our time, which
has a significant impact on various fields of science and industry, including medicine, pharmacy, and
education. The use of AI in the pharmaceutical industry opens new opportunities for improving the
processes of research, development, testing, and production of drugs. Thanks to machine learning
(ML) and deep learning (DL) algorithms, it has become possible to significantly reduce the time and
costs of developing new drugs, optimize the processes of molecule synthesis, predict the clinical and
pharmacological properties, efficacy, and safety of drugs, and personalize patient treatment [11].
The development of AI in the medical and pharmaceutical sector is marked by an increase in
the efficiency of production processes, increased accuracy of diagnostics, prediction of side effects
and creation of innovative pharmacotherapeutic solutions. For example, through the analysis of large
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volumes of biological and clinical data, AI helps to identify new targets in the pharmaceutical design
of drugs, predict their interaction with potential molecules [12].
This article reviews the main areas of application of AI in personalized medicine,
pharmaceutical design and education, its advantages, challenges, and development prospects.
The purpose of the study was to analyze the modern use of AI in personalized medicine,
pharmaceutical design, and education. In the processes of searching, developing, testing, and
personalizing new drugs. The study is aimed at identifying key areas of application of AI, assessing
its advantages and challenges, as well as predicting future development trends in this area.
Materials and methods. The study is based on an analysis of scientific publications covering
the use of AI in medicine, pharmaceutical design, and education. A review of literature sources was
conducted, including peer-reviewed articles containing research results using machine learning (ML),
deep learning (DL) and natural language processing (NLP) algorithms in the creation and
optimization of medicines. Methods of comparative analysis, synthesis and generalization of the
information obtained were used to assess the effectiveness of the use of AI in pharmaceutical research.
The study of the article is a fragment of research works of Private Scientific Institution
"Scientific and Research University of Medical and Pharmaceutical Law" and Danylo Halytsky Lviv
National Medical University on the topic "Diagnosis, treatment, pharmacotherapy of inflammatory,
traumatic and onco-thoracic pathology using instrumental methods" (state registration number
0125U000071, implementation period 2025-2031) and "Multidisciplinary research of post-traumatic
stress disorders during war among patients (primarily combatants)" (state registration number
0124U002540, implementation period 2024-2029); and "Interdisciplinary scientific and
methodological research in the field of pharmaceuticals and veterinary medicine: innovations,
modernization, technologies, regulation" (state registration number 0125U000598, implementation
period 2025-2031).
Results and discussion.
Fundamentals of AI and its technologies
AI is an interdisciplinary field of computer science that aims to create systems that can imitate
human intelligence. The main components of AI are machine learning (ML), deep learning (DL) and
natural language processing (NLP). They allow you to model complex processes, analyze large data
sets and make predictions, which is extremely important in the pharmaceutical industry.
In the field of pharmaceutical design and development of new drugs, AI is used to identify
potential drug targets, automate testing processes and optimize clinical and pharmacological models,
personalized pharmacotherapy regimens, and train educational personnel for this area. Through the
analysis of genomic and proteomic data, AI algorithms contribute to the personalization of therapy
and the creation of innovative drugs [13].
Machine Learning, Deep Learning, and Natural Language in the Context of Pharmaceutical
Design
Machine learning (ML) is a fundamental approach in AI that allows systems to learn from
data without explicit programming. In the pharmaceutical industry, ML is used to predict molecular
properties, model drug interactions, and analyze treatment efficacy.
Deep learning (DL), a subset of ML, uses artificial neural networks to analyze complex
relationships in medical data. Its applications in pharmaceuticals include automatic image analysis,
which helps in diagnosing diseases from medical images [14].
Natural language processing (NLP) is used to analyze text data, which is useful in studying
medical records, analyzing side effect reports, and classifying scientific publications. NLP
technologies allow for the automatic processing of large amounts of information and the discovery
of new patterns in pharmaceutical research [12].
AI Algorithms and Tools in Medical Data Analysis
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AI is actively used for medical data analysis due to a wide range of algorithms and
technologies.
Key AI algorithms are shown in Fig. 1.
Finding new drug molecules using AI
Traditional methods of drug discovery are based on lengthy experimental studies. However,
AI allows for significantly accelerating this process. Using deep learning algorithms and
computational chemistry methods, it is possible to predict the interaction of molecules with biological
targets even before laboratory experiments are conducted.
One of the most effective approaches is the use of generative models, such as deep neural
networks (DNNs) and autoencoders, that generate new chemical structures with desired properties.
For example, the DeepChem and AtomNet algorithms analyze huge databases of known drug
compounds and predict new promising molecules [11].
In addition, AI-based structural analysis methods are used to predict the interaction of ligands
with protein targets, which significantly increases the efficiency of screening potential new drugs
[14]. Optimization of clinical trials and minimizing errors
Clinical trials are the most expensive and time-consuming stage of drug development. AI
helps to optimize this process, reducing the number of experiments required and increasing the
accuracy of results.
The main capabilities of AI in clinical trials are shown in Fig. 3.
Fig. 1. Key AI algorithms.
Popular AI tools in medicine and pharmaceuticals include four, as shown in Fig. 2.
Regression models – used to predict clinical and pharmacological
parameters of new drugs
Clustering methods
– help to identify patterns in large data sets, for
example, to stratify patients by type of
response to pharmacotherapy
Genetic algorithms
– used to optimize the chemical structure of
molecules and search for new medicinal
compounds
Deep neural networks
– allow for the analysis of images from
medical scans, which contributes to more
accurate diagnosis of diseases [14]
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Fig. 2. Popular AI tools in medicine and pharmacy.
AI-based systems such as IBM Watson analyze historical data on clinical trials and help plan
new experiments, reducing their duration and costs [15].
Predicting clinical and pharmacological properties and toxicity of new drugs
The clinical-pharmacological properties and toxicological profile of a drug are important
parameters for determining its safety and efficacy. AI allows predicting these properties even before
laboratory studies are conducted, which significantly accelerates the process of pharmaceutical design
and drug development.
Key methods for predicting the safety and efficacy of new drugs are shown in Fig. 4.
Studies have shown that the use of Random Forest and Support Vector Machines algorithms
allows predicting the toxicity of new drugs with an accuracy of over 85% [15].
Thus, AI plays a key role in modern pharmaceuticals, pharmaceutical design. It allows
significantly reducing the time for developing new drugs, optimizing clinical trials, and ensuring
safety. effectiveness.
AI in personalized medicine and pharmaceuticals
Personalized medicine is a modern approach to pharmacotherapy, which is based on the
individual characteristics of the patient, including his genetic profile, lifestyle, and other factors. AI
opens new opportunities for personalizing pharmacotherapy. It allows predicting the effectiveness of
drugs for specific patients and reducing the risks of side effects.
TensorFlow and
PyTorch
– libraries for building deep neural networks
AutoML
– machine learning automation platform
DeepChem
– a set of tools for analyzing chemical data
using AI
IBM Watson Health
– system for analyzing medical texts and
predicting treatment effectiveness
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Fig. 3. Key AI capabilities in clinical trials.
Fig. 4. Key methods for predicting the safety and efficacy of new drugs.
Personalized treatment using genetic data analysis
Genetic tests allow determining how the patient's body will react to certain drugs. The
introduction of AI into the analysis of genomic data allows for faster and more accurate identification
of biomarkers responsible for the effectiveness and safety of drugs. The main areas of application of
AI in personalized medicine and pharmaceuticals are shown in Fig. 5.
Research participant recruitment.
Using NLP to
analyze patient medical records allows you to find
candidates with the right characteristics
Predicting outcomes. Machine learning
algorithms analyze data on the efficacy and safety
of drugs in previous trials, predicting their effects
on different patient groups
Reducing the risk of data bias. AI automatically
detects anomalies and possible errors in the
collected information, which improves the quality
of research
ADMET modeling (Absorption, Distribution, Metabolism, Excretion,
Toxicity) modeling. Using neural networks and regression models to
predict the behavior of a molecule in the body
Genetic algorithms and deep neural networks that analyze chemical
structures and predict possible toxic effects
Using big data to identify patterns in pharmacokinetic studies
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Fig. 5. Main directions of application of AI in personalized medicine and pharmaceuticals.
Thanks to deep learning algorithms, such as AlphaFold, rapid modeling of protein structures
becomes possible, which helps in the development of individual drugs [16].
Increasing the effectiveness of treatment through individual approaches
AI allows you to create individual treatment strategies, pharmacotherapy, which consider the
unique characteristics of each patient.
Main capabilities of AI in personalized medicine and pharmaceuticals are shown in Fig. 6.
Fig. 6. Main capabilities of AI in personalized medicine and pharmaceutics.
Pharmacogenomics – the study of genetic variations that affect the
metabolism and effects of drugs. For example, certain mutations in
CYP450 genes may determine how effectively a patient's body
metabolizes antidepressants or anticancer drugs [16]
Multivariate disease analysis. AI analyzes large amounts of patient data,
identifying genetic variations that cause diseases such as cancer, diabetes,
or cardiovascular diseases
Genome editing and CRISPR.
AI helps predict gene therapy outcomes,
enabling precise correction of genetic defects
Predicting response to pharmacotherapy. For example, in the treatment
of cancer, AI analyzes the genetic profile of the tumor and selects the
most effective drug
Drug impact modeling. Virtual clinical trials help predict how a
particular drug will affect a patient's body before it is prescribed
Recommender systems for doctors. AI helps doctors make informed
decisions by analyzing vast amounts of clinical data
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Such approaches are already used in the treatment and pharmacotherapy of rare diseases, when
standard methods do not give the expected effect. For example, AI is used to select pharmacotherapy
for cystic fibrosis and neurodegenerative diseases [16].
Thus, AI makes personalized medicine and pharmaceutics more accessible and effective,
allowing to adapt treatment and pharmacotherapy to the individual needs of patients and reducing the
risks of complications.
AI in the pharmaceutical design of new drugs
AI plays an important role in improving the development, production, and testing of new
drugs. Its algorithms can optimize each stage of drug creation: from automating the synthesis of
substances to monitoring the quality of finished products. Thanks to AI, pharmaceutical companies
reduce the time for drug development, minimize errors and improve safety standards.
Automation of production and quality control
The introduction of AI into the production processes of the pharmaceutical industry allows to
significantly increase the efficiency and accuracy of technological operations.
The main advantages of AI in the production of medicines are shown in Fig. 7.
Fig. 7. Key benefits of AI in drug manufacturing.
This approach not only improves the quality of drugs, but also ensures compliance with the
requirements of regulatory authorities such as the FDA and EMA.
AI in optimizing testing and safety monitoring processes
Pharmaceutical testing is a long and complex process that includes preclinical studies, clinical
trials, and post-marketing monitoring. The implementation of AI significantly accelerates the analysis
of the safety and efficacy of drugs.
The main capabilities of AI in drug testing are shown in Fig. 8.
Robotization of production lines.
Intelligent control systems ensure
precise dosage of components,
temperature and humidity control
during drug production [1]
Defect prediction. AI analyzes
production process data in real time
and detects potential defects before
products go on sale
Waste reduction. Algorithms
optimize raw material usage and help
reduce production losses
Quality control. Machine vision and
neural networks check finished
dosage forms for compliance with
regulatory requirements, detecting
even microscopic defects
Benefits of AI in drug
production
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Fig. 8. Key AI capabilities in drug testing.
Companies are already using tools such as IBM Watson and BenevolentAI to analyze clinical
trial data and predict the effectiveness of new drugs [15].
Thus, AI can significantly reduce testing time, reduce the risks of failed trials, and ensure safer
entry of new drugs into the market.
Regulation and ethics of AI in medicine and pharmacy
The development of AI in the pharmaceutical industry opens significant opportunities for
improving diagnostics, treatment, pharmacotherapy, and drug production. At the same time, the use
of AI in medicine and pharmacy raises several ethical questions and requires clear regulatory
approaches to ensure safety, effectiveness, and fairness.
Ethical issues of using AI in medicine and pharmacy
The main ethical challenges of implementing AI in the pharmaceutical sector are (Fig. 9).
The ethical principles of using AI in medicine and pharmacy should be based on ensuring
fairness, safety, responsibility and explainability of algorithmic decisions.
The impact of AI on industry standards and regulation
Regulatory bodies are already beginning to actively respond to the growing role of AI in
medicine and pharmacy.
The main areas of regulation are shown in Fig. 10.
Virtual modeling. Deep learning
algorithms predict potential risks early in
drug development, reducing the need
for animal testing
Optimization of clinical trials. AI
analyzes patients’ medical histories and
genetic data, helping to select optimal
groups for studies, which reduces costs
and improves the quality of the results
obtained [12]
Automated analysis of side effects. AI
systems scan large sets of clinical data
and registration documents, identifying
adverse reactions and possible drug
interactions
Post-registration safety monitoring. AI
analyzes data from electronic medical
records, social networks, and patient
forums, identifying new side effects of
drugs
AI capabilities in drug
testing
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Fig. 9. The main ethical challenges of implementing AI in medicine and pharmacy.
Fig. 10. Main areas of regulation.
Certification of AI models. Authorities such
as the FDA (US) and EMA (EU) are
developing standards to assess the safety
and effectiveness of AI algorithms in
pharmaceuticals. For example, the FDA has
already approved some AI algorithms for
medical image analysis [12]
Data protection compliance. Regulatory
requirements such as GDPR and HIPAA
require companies to ensure the security of
patient personal data used by AI
Post-authorization monitoring. AI solutions
should be under constant supervision to
identify possible risks and negative
consequences of their use in pharmaceutics
Development of international standards.
The World Health Organization (WHO) and
the International Conference on
Harmonization (ICH) are working to unify
approaches to implementing AI in
pharmaceutics
Main areas of regulation
Transparency and explainability. Many AI algorithms operate as “black
boxes”, making it difficult to explain their decisions to doctors, patients,
and regulators [11]
Algorithm bias. AI systems can reproduce and reinforce existing biases
in medical research due to underrepresentation of training data. This can
lead to unequal access to treatment for different patient groups
Data privacy and security. AI processes large amounts of personal
information, which requires enhanced protection of patient data and
regulatory compliance (e.g. GDPR in the EU, HIPAA in the US)
Allocation of liability. In the event of erroneous AI recommendations in
pharmaceutical developments or medical decisions, the question arises:
who will bear responsibility – the algorithm developer, the
pharmaceutical company, or the doctor?
Substitutability of human expertise. There are concerns that automation
of processes may reduce the role of doctors and pharmacists in decision-
making, which could negatively affect trust in medicine
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In general, AI regulation in medicine and pharmacy should be dynamic, consider rapid
technological changes, balancing innovation, and protection of public interests.
Future use of AI in medical and pharmaceutical research
AI has significant potential for revolutionary changes in the field of healthcare. Further
development of deep learning (DL), machine learning (ML) and natural language processing (NLP)
technologies opens new opportunities for reducing the time and cost of drug development [17].
One of the key areas of development is personalized medicine and pharmaceuticals, which is
based on the analysis of genetic data and individual biological markers. The use of AI allows for the
creation of personalized treatment regimens and optimization of drug dosages [18].
In addition, considerable attention is paid to the use of AI in the study of new antiviral agents,
which allows for the analysis of immune repertoires and the creation of effective vaccines [19].
Technical and Regulatory Barriers to Widespread AI Adoption
Despite the great promise, the large-scale adoption of AI in medicine and pharmaceuticals
faces several technical and regulatory hurdles.
❖ First, the quality and availability of large datasets are critical for effective training of AI
models. However, significant limitations in standardization of medical data complicate their
use in pharmaceutical research [12, 17].
❖ Second, the issue of ethics and data privacy remains one of the biggest challenges. The use of
personalized patient information to train AI algorithms raises concerns about data security,
especially in the context of GDPR and other regulatory requirements.
In addition, regulatory authorities do not yet have clear protocols for assessing the
effectiveness and safety of AI-assisted pharmaceutical developments. The lack of harmonized
standards and methodologies for validating AI models may slow down their implementation in the
pharmaceutical sector [20].
However, despite the above difficulties, the gradual improvement of AI technologies and the
development of new regulations will contribute to the further integration of artificial intelligence into
the medical and pharmaceutical industry, opening new opportunities for the development of
innovative personalized therapeutic approaches.
The role of AI in the future development of medicine, pharmacy, and education
AI plays an increasingly important role in the pharmaceutical industry, pharmaceutical design.
AI offers new approaches to the search, development, and optimization of new drugs. Due to the
ability to process large amounts of data and analyze complex biological interactions, AI significantly
reduces the time and costs of developing new drugs [1, 9].
The development of machine learning algorithms contributes to increasing the efficiency and
accuracy of predicting clinical and pharmacological, pharmacokinetic and pharmacodynamic
parameters. It allows you to avoid potential toxic effects and select optimal dosages of drugs [2, 10].
In addition, AI plays a key role in the development of personalized medicine and
pharmaceuticals. It allows you to adapt therapeutic strategies to the genetic characteristics of each
patient [21]. The role of AI in education, training of medical and pharmaceutical personnel to master
the latest learning technologies for use in personalized medicine, pharmaceutical design of new drugs
is undeniable [17].
Prospects for patients, healthcare professionals and pharmaceutical professionals
The introduction of AI into healthcare and pharmaceutical practice has a significant positive
impact on the quality of healthcare and pharmaceutical provision. For patients, this means access to
more effective, safe, high-quality medicines, as well as the possibility of receiving individualized
pharmacotherapy regimens based on their genetic and clinical data [14, 21].
Healthcare professionals are provided with powerful tools for diagnosing and predicting the
development of diseases, which allows for more accurate and timely treatment. For example, AI
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algorithms already help in the diagnosis of respiratory diseases such as asthma, COPD and pulmonary
fibrosis by analyzing CT and X-ray images [13, 22].
However, the introduction of AI is also accompanied by certain challenges, in particular the
need to regulate the ethical and regulatory aspects of its use. Particular attention is required to the
issue of personal data protection, as patients must be confident in the security of their medical records
[22]. In general, the use of artificial intelligence in pharmaceuticals and medicine opens up new
prospects for increasing the effectiveness of treatment and improving the quality of life of patients.
Further development of this technology will depend on the successful overcoming of regulatory and
ethical barriers, as well as on the integration of AI into everyday medical and pharmaceutical practice,
pharmaceutical design and education [23, 24].
Conclusions. AI is significantly transforming medicine, the pharmaceutical industry and
training for these areas. It accelerates the processes of discovery, development, testing and
personalization of medicines. The use of AI helps to optimize the search for new drugs, increase the
efficiency of clinical trials, predict the clinical, pharmacological and pharmacokinetic properties of
drugs. It allows to reduce costs and reduce the time to market for medicines. Personalized medicine
and pharmaceuticals, based on the analysis of genetic data using AI, provide an individualized
approach to treatment, pharmacotherapy, and reduce the risks of complications. At the same time, the
further development of AI technology will depend on improving the regulatory framework, ensuring
the ethics of using algorithms and their integration into medical and pharmaceutical practice. Despite
the challenges, AI opens up new opportunities for creating innovative therapeutic approaches and
improving the quality of life of patients. Training medical and pharmaceutical personnel using AI
technologies contributes to the implementation of personalized medicine, innovative pharmaceutical
design methods in the development of new drugs. Further research is ongoing.
Declaration of conflict interest. The authors declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this article. The authors confirm that they
are the authors of this work and have approved it for publication. The authors also certify that the
obtained clinical data and research were conducted in compliance with the requirements of moral and
ethical principles based on medical and pharmaceutical law, and in the absence of any commercial or
financial relationships that could be interpreted as potential conflict of interest.
Funding. This research wasn’t funded by any private or governmental body.
Data availability statement. The datasets analyzed during the current study are available from
the corresponding author on reasonable request.
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23. The TED-Ed Student Talks program provides free, customizable events for educators to
support their students in discovering, developing, and sharing their ideas with each other and the
world. TedEd. URL:
https://ed.ted.com/student_talks?utm_medium=website_sailthru&utm_source=talk_exitintent&utm
_campaign=ted-ed-apps&utm_content=march15cycle
24. Can AI help develop new drugs? Ted. URL:
https://www.ted.com/talks/aviv_regev_can_ai_help_develop_new_medicines
