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11.3 AI/ML Landscape in Drug Design 243
30 for experimental bioassay validation. From this set, 10 compounds showed significant inhibitory
effects on a particular enzyme [89]. ML offers a powerful way to VS and generally involves assembling
a filtered training set of known active and inactive compounds. Using this data, ML algorithms develop
the ability to prioritize active databases of molecules against specific protein targets [90, 91].
11.3.2.4 Quantitative Structure–Activity Relationship Models
The use of AI and ML in developing QSAR models represents a significant evolution in drug discovery
and molecular design. AI and ML are used to develop and optimize QSAR models by combining wet
experiments (providing experimental data and reliable validation) with molecular dynamics simula-
tion (providing mechanistic interpretation at atomic/molecular levels) [92]. In recent years, various AI
and ML algorithms have been used to analyze the current state of the drug development process and
model molecular data and biological problems, especially through research on QSAR [93]. In modern
drug discovery, combining chemoinformatics and QSAR modeling has emerged as a powerful alliance
that enables researchers to exploit the broad potential of ML techniques for predictive molecular
design and analysis [35]. In general, two of the most successful ML algorithms QSAR applications are
random forests and DNNs. The work by Svetnik and colleagues, published in the Journal of Chemical
Information and Computer Sciences in 2003, is one of the first examples of the use of random forest in
QSAR and has subsequently been frequently used as a gold standard [94]. The rise of deep learning
and widely accessible chemical databases has improved QSAR performance. This paper proposes a
new deep learning-based method to implement QSAR prediction by combining end-to-end encoder–
decoder model and convolutional neural network (CNN) architecture [95].
11.3.2.5 Drug Repurposing
AI and ML technologies are transforming the drug repositioning process and making significant
contributions. Drug repurposing allows existing drugs to be reused for new medical indications,
and this process becomes more cost-effective and faster, thanks to AI and ML. AI and ML
approaches have been developed to identify drug repositioning candidates based on big data
sources systematically. This speeds up the drug development process with computational methods
and reduces risks [96]. Structure-based drug repositioning is a popular in silico repositioning
approach. This review discusses traditional and modern AI-based computational methods and
tools applied at various stages for SBDD pipelines. In addition, the role of generative models in
producing molecules with skeletons is highlighted [97]. Researchers focus on supervised ML and
AI methods that use publicly available databases and information sources. Although most of the
exemplary applications are in anticancer drug therapies, the methods and resources reviewed are
also broadly applicable to other indications, including COVID-19 treatment. Particular emphasis
has been placed on comprehensive target activity profiles that will enable a systematic reposition-
ing process by expanding the target profile of drugs [96]. Computational drug repositioning meth-
ods adapt AI algorithms to discover new applications of approved or investigational drugs [98].
These various applications and methods demonstrate how AI and ML are used in drug reposi-
tioning processes and how these technologies offer significant potential in drug discovery and
development. In particular, structure-based drug repositioning and supervised ML and AI meth-
ods can make drug repositioning processes more effective, rapid, and cost-effective, enabling faster
development of effective treatments for new medical indications.
The use of AI and ML technologies in drug repositioning studies in the context of the COVID-19
pandemic has a vital role in developing treatment strategies. AI- and ML-based drug repositioning
strategies enable the integration of various modules to identify practical and usable drugs and drug
combinations for COVID-19 treatments. These modules include virus–host interactions and
11 AI/ML Approaches in Drug Design244
protein–protein interactions [99]. Another study reviews the current status of applications of AI
and ML, especially in structural biology, drug repositioning, and development, stating that it will
motivate researchers to use the potential of AI in the fight against COVID-19 [100].
AI-assisted drug repositioning is seen as promising in developing new treatments for
COVID-19 infection, and accumulated biological data profiles are facilitating AI-based drug repo-
sitioning efforts for developing COVID-19 therapies [101]. The drug repositioning or repositioning
technique involves using existing drugs for emerging and challenging diseases, including
COVID-19, and this approach has become a promising approach due to the opportunity to reduce
development timelines and overall costs [102]. Some ML and AI approaches have been developed
to systematically identify drug repositioning leaders based on big data sources, further accelerating
and de-risking the drug development process by computational tools [96]. These studies and analy-
ses provide valuable insights into how AI and ML technologies can support and accelerate drug
repositioning efforts in the fight against COVID-19.
11.3.3 Challenges and Failures
Situations where AI and ML fail to meet expectations in drug discovery are generally related to the
limitations and misapplication of these technologies. AI and ML algorithms work based on accu-
rate and regularly prepared data. If the data is not prepared or modeled correctly, these algorithms
can produce misleading results, leading to severe problems in the drug discovery process [103]. AI
and ML have caused some expensive and controversial failures in the pharmaceutical industry.
Some applications of AI and ML in the industry have been major disappointments, such as Watson
AI’s automated disease diagnosis and the clinical trial failure of Exscientia’s DSP-1181 [104]. One
of the main reasons why AI and ML algorithms fail in drug development programs is the lack of
data and sparse data. A sufficient amount of data is needed to bring any drug to market, and the
lack of this data severely limits the effectiveness of AI and ML [10]. Providing data diversity is criti-
cal for an effective model. AI platforms must be trained on various datasets representing the organ-
ization’s target populations. Otherwise, these platforms may adopt fundamental biases, misread
side-by-side features, and provide misleading results [103]. These limitations and misapplications
may cause AI and ML to fail to meet expectations in drug discovery. To fully realize the potential
of AI and ML in this field, several important factors, such as proper data management, right algo-
rithm selection, and appropriate model validation, must be considered (Table 11.1).
11.4 Ethics, Reliability, and Regulatory Issues
The use of AI and ML in drug discovery raises various issues and controversies around ethical,
reliability, and regulatory issues. Ethical and trustworthy issues generally include topics such as
transparency of algorithms, data sharing, cybersecurity, and potential discrimination. Regulatory
issues include the auditing, certification, and permitting processes required to ensure that AI and
ML applications comply with standards, laws, and rules.
Data privacy: Patients’ and participants’ personal data privacy is a central issue in data-based
research. Data privacy protects individuals’ personal information from unauthorized access, use,
or disclosure. Breach of data privacy can jeopardize individuals’ privacy, security, and reputation.
Therefore, institutions that collect and use data must take the necessary precautions to protect the
data and ensure that the data is used following ethical rules [105].
245
Informed consent: When collecting and using data, individuals are required to understand and
consent to what type of information is being collected and how this information will be used [106].
Informed consent is essential to protect individuals’ rights regarding the use of their data. Lack or
inadequacy of informed consent may lead to violations or misdirection of individuals’ consent.
Therefore, institutions that collect and use data must inform individuals about their data’s purpose,
scope, duration, and consequences and obtain their consent.
Algorithm reliability: AI and ML algorithms’ accuracy, reliability, and security are critical in
drug discovery and development [107]. Algorithms must not produce false positive or false nega-
tive results and must identify safe and effective treatment options. To ensure algorithm reliability,
the quality, quantity, and diversity of the algorithms’ training data must be controlled; the perfor-
mance of algorithms should be tested; errors in algorithms must be identified and corrected; and
the risks of algorithms must be evaluated and minimized [108].
Algorithm transparency: It is essential for ethical and legal requirements to ensure that algo-
rithms work and that decision-making processes are open and transparent [109]. Algorithm trans-
parency increases the accountability of algorithms, allows algorithms’ decisions to be audited,
Table 11.1 Benefits and challenges of artificial intelligence and machine learning in drug design.
Benefits Challenges
It accelerates the drug development process, reduces its
cost, and increases the success rate
Data quality, security, and privacy issues may
occur
It offers personalized treatment options and provides
early diagnosis of diseases
Ethical, legal, and regulatory standards may
be unclear
It discovers new disease mechanisms and opens new
treatment areas
May require human expertise and supervision
Benefits: The drug development process is usually very long, expensive and risky. AI and ML optimize this process,
delivering better medicines to more patients. They contribute to every stage of the drug development process. By
analyzing biological changes caused by diseases, it identifies target molecules to intervene. It uses large datasets to
predict drug candidates’ effectiveness, safety, and side effects. It synthetically produces and optimizes new drug
molecules through techniques such as generative models. It improves the design, management, and analysis of
clinical trials. Personalized treatment options ensure that patients receive the most appropriate medication
according to their genetic, biological, and environmental characteristics. Early diagnosis of diseases increases the
chance of success of treatment. AI and ML play an essential role in providing personalized treatment options. They
find biomarkers for diagnosing diseases. By analyzing patients’ data, it determines the best treatment protocol and
monitors the effects and side effects of the treatment. Discovering new disease mechanisms helps find new
treatment targets and methods. New treatment areas offer solutions to previously untreatable or difficult-to-treat
diseases. They play an essential role in discovering new disease mechanisms. For instance, it detects genetic or
epigenetic changes caused by diseases. It reveals the relationships between diseases and suggests new drug targets
or combinations.
Challenges: Data quality, security, and privacy are critical in drug development. AI and ML require large amounts
of data. This data must be accurate, up-to-date, secure, and confidential. There may be problems with data quality,
security, and privacy. For example, data may be incomplete, inaccurate, or inconsistent. It may be subject to
cyberattacks or misuse. Disputes may arise regarding ownership or sharing of data. Ethical, legal, and regulatory
standards are the rules that must be followed in drug development. AI and ML may violate these rules or leave
them unclear. There may be problems with ethical, legal, and regulatory standards. For example, who owns the
patent rights to drugs produced by AI? How can we prove the safety of drugs produced by AI? Who has the ethical
responsibility for drugs produced by AI? Human expertise and control are indispensable in drug development. AI
and ML cannot replace humans or question their decisions. There may be problems with human expertise and
control. For example, how to control the side effects of AI-generated drugs? How to explain the working
mechanism of drugs produced by AI? How do we test the reliability of drugs produced by AI?
11 AI/ML Approaches in Drug Design246
helps algorithms justify their decisions, and enables the algorithms’ decisions to be challenged. To
ensure algorithm transparency, sufficient information should be shared about the design, training,
testing, implementation, and results of algorithms. Participation and feedback of the parties
affected by the decisions of the algorithms should be ensured. The algorithms’ decisions must be
shown to comply with ethical and legal standards [110, 111].
Interpretability: AI and ML algorithms must be able to present their decisions and recommen-
dations in a format that humans can understand and evaluate. Interpretability increases algo-
rithms’ reliability, transparency, and accountability; it explains the logic, significance, and
consequences of algorithms’ decisions. It helps evaluate the accuracy, consistency, and fairness of
algorithms’ decisions. Presenting the data, features, parameters, weights, coefficients, and other
factors behind the algorithms’ decisions in an understandable way to ensure interpretability;
explaining the algorithms in a language appropriate to the parties their decisions affect. The deci-
sions of the algorithms need to be supported by statistical criteria such as confidence interval,
sensitivity, accuracy, and specificity [5].
Current regulations: Regulatory authorities such as the FDA (Food and Drug Administration)
have developed regulations and established specific standards and approval processes regarding
how AI and ML can be used in drug discovery and development [112].
Future prospects: A matter of great interest is how the regulatory landscape will change as AI
and ML rapidly evolve, and these technologies are increasingly used in drug discovery and develop-
ment. Clear standards, guidelines, and approval processes are expected in the future. These factors
lead to meaningful discussions and considerations about how and when to apply AI and ML in drug
discovery and development in an ethical and regulatory manner. By collaborating and discussing
these issues, regulatory authorities, industry stakeholders, and the academic community are trying
to determine how to apply AI and ML in this field safely, ethically, and regulatory-compliantly.
11.5 Future Directions
The rapid development of AI and ML technologies poses various directions and expectations on
how they can be applied in drug discovery and development. It is thought that quantum computing,
one of the emerging technologies, can be much faster and more effective than traditional molecular
modeling and simulation calculations. Quantum computing can develop more effective algorithms
for designing and optimizing drug molecules. In addition, blockchain technology is thought to
transform drug discovery and development processes by providing data security, traceability, and
transparency. In genomics and proteomics integration, AI and ML can be integrated with fields
such as genomics and proteomics, providing a better understanding of disease mechanisms and
more effective identification of target molecules. This integration may make it possible to develop
more effective and personalized treatment strategies. AI and ML solutions must consider sustaina-
bility factors such as energy efficiency, modular design, and scalability. In addition, these solutions
must be scalable to meet the requirements of large datasets and computational resources.
These guidelines and emerging technologies offer essential insights into how AI and ML can be
made more effective, sustainable, and scalable in drug discovery and development. The applicabil-
ity and potential of AI and ML in these fields require broad discussion and evaluation of how and
when to apply these technologies ethically and regulatory-compliantly. In addition, how these
technologies can be made more effective and reliable in drug discovery and development and inte-
grated with various interdisciplinary approaches and emerging technologies will require more
research and collaboration.
eferences 247
11.6 Conclusion
This book chapter overviews the importance, applications, methods, and challenges of AI and
ML methods in drug design. AI and ML improve and accelerate drug candidates’ discovery,
design, optimization, and evaluation processes by providing data-based and computer-aided
methods in drug design. AI and ML overcome the challenges faced by traditional methods
used in drug design and enable the development of more effective, safer, and cheaper drugs.
These methods can perform processes such as identifying drug targets, constructing molecular
structures, predicting molecular properties, modeling molecular interactions, analyzing drug
similarity and diversity, and evaluating drug safety and toxicity. AI and ML can also perform
operations such as data cleaning, data integration, data visualization, data mining, and data
analysis to improve the quality and reliability of data used in drug design. However, some limi-
tations exist when using AI and ML methods in drug design. AI and ML methods often require
large amounts of data, but the data used in drug design may be limited, noisy, or incomplete.
AI and ML methods can also produce complex or black-box models, but interpretability and
confirmability of models used in drug design are essential. Finally, AI and ML methods can
cause ethical, legal, and social problems. AI and ML methods may violate data privacy, secu-
rity, and ownership of copyrights or patents.
Therefore, for more effective use of AI and ML methods in drug design, it is recommended that
future research should focus on the following topics:
● Leveraging new data sources or integrating existing data sources to improve the quality, cover-
age, and accessibility of data used in drug design.
● Developing new algorithms or frameworks or improving existing ones to increase the interpret-
ability, confirmability, and reliability of AI and ML models used in drug design.
● Creating new policies or rules or implementing existing ones to address ethical, legal, and social
issues of AI and ML methods used in drug design.
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