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11.1 Introduction
Historically, drug design and development have been conducted through experimental methods
and chemical synthesis techniques [1, 2]. While these methods provide understanding at the
molecular level, they are often time-consuming, costly, and limited, especially in the selection of
starting molecules [3]. In recent years, the rise of computational methods has played a significant
role in accelerating drug design processes [4–6]. These computational methods include many dif-
ferent technologies, such as molecular dynamics simulations, structure-based design (SBDD), and
ligand-based design (LBDD) [7–9]. The adaptation of artificial intelligence (AI) and machine
learning (ML) technologies in this field has enabled these methods to find a wider field of applica-
tion and especially to analyze large datasets quickly and effectively [10, 11]. The contribution of AI
and ML to drug design enables in-depth knowledge of particularly complex biological systems and
their vast chemical space [12]. This offers an excellent opportunity for faster and more accurate
identification of potential drug candidates [13] (Figure 11.1).
11.2 Traditional Drug Design Methods
Traditional drug design is mainly based on experimental laboratory methods, in vitro and in vivo
testing, and chemical synthesis processes [4, 14]. These methods have formed the basis of the phar-
maceutical industry over the years and have led to many essential drug discoveries [15].
Experimental methods such as high-throughput screening (HTS) make it possible to test thou-
sands of chemical compounds [16] quickly. However, these methods are generally expensive and
only suitable for small molecular changes [17]. The effectiveness and safety of drug candidates are
evaluated using cell cultures or animal models [18]. These tests are critical for monitoring the bio-
logical effects of potential drugs but can be time-consuming and raise ethical issues [19]. Drug
candidates are often produced through chemical synthesis [20, 21]. However, this process is often
complex and multistage, leading to constraints in terms of cost and time. Although traditional
methods enable many drug candidates to pass the initial stages, new and more effective methods
need to be investigated due to the limitations of these methods [22].
11
AI/ML Approaches in Drug Design
Kevser Kübra Kırboğa
Faculty of Engineering, Bioengineering Department, Bilecik Seyh Edebali University, Bilecik, Türkiye
11 AI/ML Approaches in Drug Design238
11.2.1 The Rise of Computational Methods
The limitations of traditional drug design methods have paved the way for the rise of computational
methods [23, 24]. These methods include molecular modeling, simulations, and algorithm-based
calculations [25].
Molecular modeling enables the study of chemical and biological systems at the atomistic
level [26]. This technique is critical for understanding the interactions of potential drug molecules
with target proteins [27]. Simulation techniques such as molecular dynamics simulations are used
to understand the dynamics of biological systems [28]. These simulations may allow observing
molecular interactions change over time [29, 30]. Simulation techniques can explain molecular
systems’ behavior, functions, mechanisms, and techniques to study conformational changes of
drug targets, drug–binder interactions, drug–target residence times, drug resistance mechanisms,
or drug side effects [31, 32].
Algorithm-based calculations analyze and model large datasets [33]. These algorithms can
quickly analyze complex biological and chemical datasets, thus accelerating the drug design
process [34]. Algorithm-based calculations can be applied at different levels and for different
purposes in drug design. Algorithm-based calculations are used to understand complex inter-
actions in biological systems for the identification of drug targets, perform intelligent searches
in chemical space to establish molecular structures, calculate physicochemical parameters to
predict molecular properties, use structural information to model molecular interactions, cre-
ate chemical fingerprints for analysis of drug similarity and diversity, and can perform pro-
cesses such as predicting ADMET profiles to evaluate drug safety and toxicity [35, 36]. This
rise of computational methods has opened many doors for faster and more efficient drug dis-
covery. However, these methods also have limitations and challenges, highlighting the need
for future research [37].
It is shown that artificial intelligence and machine learning analyse
biological changes caused by diseases and identify target molecules
to intervene. In this way, selecting the most suitable targets for drug
development is possible
Drug design with artificial intelligence and machine learning
Identifying targets to intervene
Identifying possible drug candidates
Accelerating clinical trials
Finding biomarkers for diagnosing the disease
Artifical intelligence and machine learning are shown to improve the
design, management, and analysis of clinical trials. Techniques such as
reinforcement learning are found to use feedback mechanisms to
make the best decisions in clinical trials
Artificial intelligence and machine learning are shown to improve the design,
management, and analysis of clinical trials. With various techniques, feedback
mechanisms are used to make the best decisions in clinical trials. In this way,
the duration, cost and risk of clinical trials are reduced, while the effectiveness
and safety of drug candidates are increased
Artificial intelligence and machine learning are shown to find
biomarkers for early diagnosis of diseases. in this way, it may be
possible to offer personalized treatment options
Figure 11.1 Drug design process with artificial intelligence and machine learning.
11.3 AI/ML Landscape in Drug Design 239
11.2.2 The Importance of AI/ML in Modern Drug Design
In modern drug design and development processes, the contributions of AI and ML technologies
are becoming increasingly evident [38, 39]. These technologies play an active role in many differ-
ent stages of the drug discovery process [40]. Drug discovery and development is a long, complex,
and costly process. This process has many stages, such as identifying potential drug candidates,
characterizing, optimizing, and preparing for clinical tests. Each stage requires the generation,
analysis, and interpretation of big data. AI/ML techniques help extract meaningful information
from this data, create new hypotheses, and expand existing scientific knowledge [41]. AI/ML tech-
niques play an essential role in identifying and selecting drug targets. Drug targets are biological
molecules on which drugs act. These molecules can often be receptors, enzymes, nucleic acids, or
ion channels [42, 43]. AI/ML techniques are used in identifying disease-associated genes, proteins,
or metabolites, modeling target–effector relationships and target validation [44].
AI/ML techniques are also effective in designing and improving drug candidates. Drug candi-
dates are chemical compounds that exert a biological effect by binding to drug targets. These com-
pounds’ physicochemical and pharmacokinetic properties affect factors such as bioavailability,
efficacy, safety, and toxicity [45]. AI/ML techniques are used in the development of QSAR models
that describe the relationships between the structural features and biological activities of drug
candidates, in estimating target binding affinities of drug candidates, in optimizing the production
process of drug candidates, and in determining the toxicity profiles of drug candidates [10, 22].
AI/ML techniques contribute to accelerating and reducing the cost of the drug discovery and
development process. Thanks to AI/ML techniques, the number and time of experiments used in
the drug discovery and development process are reduced, fewer animal experiments are per-
formed, and the clinical success rate is increased. AI/ML techniques are of great importance in
modern drug design. AI/ML techniques make drug discovery and development more efficient,
safe, and economical. The accuracy and reliability of the information obtained through AI/ML
techniques are constantly improved and audited.
11.3 AI/ML Landscape in Drug Design
11.3.1 AI/MLAlgorithms and Methods
11.3.1.1 Machine Learning Models
ML models are a prominent category among AI algorithms and methods. It generally can learn
from large and complex datasets and thus has different application areas in drug design and devel-
opment processes [46, 47] (Figure 11.2). Supervised learning models are trained to predict a spe-
cific outcome. It is used to indicate the pharmacokinetic properties of a compound [48]. Supervised
learning models predict outputs in new data by learning input–output relationships in training
data. The success of supervised learning models depends on the quality, quantity, and diversity of
training data. These models are one of the ML techniques frequently used in drug design [49, 50].
Unsupervised learning models are aimed at discovering hidden structures in datasets. It is used to
understand disease mechanisms by analyzing gene expression data [51, 52]. Unsupervised learn-
ing models are used when the inputs in the training data are not labeled or the outputs are
unknown. Unsupervised learning models perform similarities, differences, clustering, or dimen-
sionality reductions in datasets. Unsupervised learning models allow the uncovering of new
knowledge in drug design [53, 54].
11 AI/ML Approaches in Drug Design240
Reinforcement learning algorithms learn to achieve a specific goal by solving decision-making
problems. It is used in drug design, especially for compound optimization [55]. Reinforcement
learning algorithms are a model in which an agent performs a specific action in an environment
and receives a reward or punishment resulting from that action. The agent’s goal is to find the best
policy of action to achieve maximum reward in the long run. Reinforcement learning algorithms
help drug design produce more effective and safe drug candidates [55, 56]. Deep learning is a sub-
set of artificial neural networks and often uses multilayer network structures. It is used in drug
design to solve complex problems such as modeling protein folding and protein–protein interac-
tions [57, 58]. Deep learning algorithms can learn complex relationships from high-dimensional
and heterogeneous data. The advantages of deep learning algorithms include high accuracy, flexi-
bility, and generalizability. The disadvantages of deep learning algorithms include high computa-
tional cost, data requirements, and lack of interpretability [59, 60]. Random forest, SVM (support
vector machine), and other algorithms are common ML algorithms often used for classification
and regression problems. Such algorithms are often used to predict the biological activities of drug
compounds [61]. A random forest algorithm is an ensemble method formed by combining multi-
ple decision trees. The random forest algorithm offers high accuracy, low variance, and high inter-
pretability [62]. The SVM algorithm is a method that tries to find a boundary that separates data
points into two or more classes. The SVM algorithm provides high accuracy, low overfitting, and
high generalizability [63, 64]. ML models offer increased flexibility and application potential in
drug design and development processes. However, these models also need to pay attention to issues
such as ethical and regulatory issues, model interpretability, and data security [65].
11.3.1.2 Neural Networks
Neural networks are among AI and ML applications’ most widely used methods. In particular,
deep learning, a type of neural network model, has great potential in solving complex problems.
Artificial neural networks represent simple mathematical modeling of biological neural networks
and contain large numbers of nodes or “neurons” [66].
Protein folding: How the three-dimensional structure of proteins is formed plays a critical
role in the functioning of biological systems. However, understanding and simulating protein fold-
ing mechanisms is a complex and challenging scientific problem. Zhao et al. (2023) have shown
Deep learning: Predicts the effectiveness, safety
and side effects of drug candidates using large
datasets
Natural language processing: Automatically
reads scientic literature, patents and clinical
reports and extracts information useful for drug
development
Some methods and techniques used by artificial intelligence
and machine learning in drug design
Generative models: Synthetically produce and
optimize new drug molecules
Reinforcement learning: Uses feedback
mechanisms to improve the performance of drug
candidates
Figure 11.2 Methods and techniques used by artificial intelligence and machine learning in drug design.
11.3 AI/ML Landscape in Drug Design 241
that deep learning models are essential in understanding and simulating protein folding
mechanisms [67]. Deep learning models can process large amounts of data to extract structural
features of proteins from their amino acid sequences and optimize their energy functions. In this
way, it allows the discovery of new and functional proteins. Deep learning models in drug discov-
ery are important for identifying potential drug candidates for targeting and inhibiting disease-
causing proteins. In addition, deep learning models produce new information on protein–protein
interactions, protein–tissue relationships, and protein diseases. Therefore, deep learning models
are powerful tools that revolutionize the field of protein folding and are useful in drug discovery.
Compound optimization: Neural networks are used to model complex relationships between
compounds’ biological activities and pharmacokinetic properties [68]. In this way, neural net-
works can assist in designing new compounds with desired properties or in optimizing existing
compounds. Neural networks can predict the binding affinities, activity spectra, selectivity, meta-
bolic stability, bioavailability, and toxicity of compounds. Neural networks can also show how
compounds are distributed in chemical space and which regions are worth exploring [69].
Target identification: Neural networks identify potential drug targets and predict their effec-
tiveness [70, 71]. In this way, neural networks can contribute to target validation, an essential step
in drug development. Neural networks can model intracellular or extracellular signaling pathways,
analyze protein–protein interactions, identify disease-associated genes or proteins, or classify drug
targets [72]. Such applications of neural networks show that they are versatile and effective tools
in drug design and development processes. However, these models’ complexity and interpretabil-
ity issues must be considered, especially in terms of ethical and regulatory issues. It may cause
issues regarding confidentiality, security, and ownership of data used in drug design. In addition,
neural networks can cause problems with the accuracy, reliability, and accountability of models
used in drug design. Therefore, it is necessary to develop appropriate standards and guidelines for
using neural networks in drug design.
Natural Language Processing (NLP) NLP is a field where the disciplines of AI and linguistics come
together. NLP also has an essential place in drug design and development processes. It can be used
to automatically extract information from text sources such as scientific literature, clinical reports,
and patent data [73].
Information extraction: NLP algorithms can automatically extract information about drug
targets or interactions from scientific literature and patent data [74, 75].
Clinical data analysis: NLP can extract data from clinical reports or patient histories and use
this data to provide valuable information about drug side effects or effectiveness [76, 77].
Sentiment analysis: Social media data or patient comments can be analyzed to understand the
effects of medications on patient experiences [78, 79].
These contributions to NLP drug design and development processes show that NLP is also very
promising in this field. However, NLP also has some challenges, such as ethical and privacy issues,
which must be addressed carefully.
11.3.2 Applications in Drug Design
11.3.2.1 Peptide Synthesis
AI and ML technologies in peptide synthesis have led to significant advances in the last five years.
Nuritas has developed a platform to discover new and therapeutic peptides by integrating AI and
deep learning with “omics” analysis. By integrating the AI prediction platform with in-house labo-
ratories, it rapidly learns and preclinically validates peptide therapeutics. The Nuritas platform com-
bines an extensive library of peptides from natural sources with publications and knowledge on
11 AI/ML Approaches in Drug Design242
disease biology, then generates peptide predictions for the selected target or disease domain.
Predictions are tested in-house in vitro, while active peptides are tested in vivo by outside partners or
contract research organizations. The results are fed back to the predictor at each verification stage,
enabling complete optimization of the predictive feedback loop [80]. In addition, Massachusetts
Institute of Technology (MIT) researchers have developed an approach combining experimental
chemistry and AI that discovers nontoxic, highly active peptides to enhance drug delivery [81].
Data-driven computational methods are accelerating the discovery and development of biopeptides,
and ML can rapidly and effectively predict the benefits of therapeutic peptides [82]. Developing
high-throughput technologies and AI have expanded ML methods for discovering new lead peptides
and incorporated them into rational drug design [83]. AI and ML have been implemented in various
drug discovery processes, including peptide synthesis, streamlining drug discovery and develop-
ment [84]. AI and ML technologies are accelerating the discovery and development of biological and
therapeutic peptides, making these processes more efficient and cost-effective and offering new
methodologies and applications in areas such as drug delivery and design. High-throughput analy-
ses and predictions are critical to understanding and optimizing peptides’ biological and therapeutic
potential, providing powerful tools to accelerate drug development processes, facilitate the discovery
and validation of peptides, and stimulate new and innovative research and applications in this field.
11.3.2.2 Molecular Design
Molecular design is a scientific process for discovering and designing new molecules with desired
properties. Molecular design has essential applications in various fields, such as drug design and
discovery, plant protection, chemical biology, materials science, and nanotechnology. Molecular
design is traditionally performed using methods such as structure- and linker-based drug design,
augmented drug design (ADD), multipurpose de novo drug design (MPO), structure–activity rela-
tionship (SAR), and big data analysis. However, these methods have some limitations. These meth-
ods are often costly, time-consuming, and complex. In addition, these methods may not fully reflect
molecular systems’ dynamicity, heterogeneity, and multidimensionality. AI is a technology with
great potential in the field of molecular design. AI can accelerate and facilitate the discovery and
design of molecules with desired properties using methods such as data analysis, ML, deep learn-
ing, machine reasoning (MR), and causal inference (CI). AI overcomes the limitations of tradi-
tional molecular design methods and enables more efficient, more reliable, and cheaper molecular
design. AI can be applied to molecular design at different levels and for other purposes [85, 86].
11.3.2.3 Virtual Screening (VS)
Virtual screening (VS) enables the prioritization of compounds to identify and test new “hit” mole-
cules, significantly reducing experimental attrition rates [87]. A fully automated AI/ML VS cascade
has been implemented at a drug discovery center in Africa. An AI- and ML-based tool called ZairaChem
has been developed for quantitative structure–activity/–property relationship (QSAR/QSPR) mode-
ling. This tool requires low computational resources and can operate on various datasets. ZairaChem
has been used for malaria and tuberculosis drug discovery, with 15 models forming a VS cascade.
These models include a variety of decision-making tests, from whole-cell phenotypic screening to
cytotoxicity, water solubility, and permeability. ZairaChem can inform the progress of frontrunner
compounds in Holistic Drug Discovery and Development Center using computational profiling before
synthesizing and testing compounds [87]. A technique that is iteratively trained with deep neural
networks (DNNs) has been developed to enable billion-sized molecular libraries for structure-based
screening. This technique introduces DNNs with small datasets and thus can screen ultra-large molec-
ular libraries [88]. The ML models screened 3601 compounds from a specific internal library, selecting