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[cor2]Corresponding author

# Explainable Artificial Intelligence for Drug Discovery and Development - A
Comprehensive Survey

Roohallah Alizadehsani<EMAIL_ADDRESS>Institute for Intelligent
Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC 3216,
Australia  Sadiq Hussain  Department of Statistics, Dibrugarh University,
Dibrugarh 786004<EMAIL_ADDRESS>Rene Ripardo Calixto  Department of
Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970,
Brazil<EMAIL_ADDRESS>Victor Hugo C. de Albuquerque  Department of
Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970,
Brazil<EMAIL_ADDRESS>Mohamad Roshanzamir  Department of Computer
Engineering, Faculty of Engineering, Fasa University, Vali asr Blvd, Fasa
74617-81189, Iran  Mohamed Rahouti  Department of Computer & Information
Science, Fordham University, Bronx, NY 10458, USA<EMAIL_ADDRESS>Senthil
Kumar Jagatheesaperumal<EMAIL_ADDRESS>Department of Electronics
and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi,
Tamil Nadu, India

###### Abstract

The field of drug discovery has experienced a remarkable transformation with
the advent of artificial intelligence (AI) and machine learning (ML)
technologies. However, as these AI and ML models are becoming more complex,
there is a growing need for transparency and interpretability of the models.
Explainable Artificial Intelligence (XAI) is a novel approach that addresses
this issue and provides a more interpretable understanding of the predictions
made by machine learning models. In recent years, there has been an increasing
interest in the application of XAI techniques to drug discovery. This review
article provides a comprehensive overview of the current state-of-the-art in
XAI for drug discovery, including various XAI methods, their application in
drug discovery, and the challenges and limitations of XAI techniques in drug
discovery. The article also covers the application of XAI in drug discovery,
including target identification, compound design, and toxicity prediction.
Furthermore, the article suggests potential future research directions for the
application of XAI in drug discovery. The aim of this review article is to
provide a comprehensive understanding of the current state of XAI in drug
discovery and its potential to transform the field.

###### keywords:

FPGA Data center Cloud Computing Hardware Accelerators Big data

##  1 Introduction

The field of drug discovery and development has started playing a significant
role in the healthcare industry, in order to find new compounds and
therapeutic targets that can successfully cure a variety of diseases [1]. The
traditional drug development method has been laborious, and resource-
intensive, and experienced major difficulties in identifying promising
therapeutic candidates over the previous few decades. However, the landscape
of drug development has undergone a radical upheaval since the advent of
artificial intelligence (AI) and machine learning (ML) technologies, which
promise to quicken and improve the process [2].

AI and ML models have shown the ability to analyze enormous datasets, discover
insightful patterns, and generate predictions for identifying prospective drug
candidates and targets. Lead optimization [3], virtual screening [4], compound
design [5], and medication repurposing [6] are just a few of the domains where
the use of AI and ML in drug development has already produced promising
outcomes. These models have the potential to greatly overshoot the success
rate of drug discovery and decrease the time and expense involved as they
become more complex and potent. As AI and ML models become more complicated, a
major issue in the area has emerged, which is the lack of transparency and
interpretability [7]. Although these models have excellent predictive powers,
it still remains challenging to explain why they make such predictions. This
lack of interpretability can make it more challenging for researchers,
doctors, and regulatory bodies to trust and accept AI and ML-driven
predictions in the context of drug discovery. Validating and ranking the
discovered targets or compounds also becomes hectic without knowledge of how
AI algorithms make decisions [8].

Such aforementioned interpretability gap could be addressed by Explainable
Artificial Intelligence (XAI), which tries to offer clear and intelligible
justifications for the predictions made by AI and ML models [9]. In addition
to increasing trust and acceptance, XAI also makes it possible for researchers
to spot any biases, inaccuracies, or limits in the underlying data or model
architecture by making it possible for humans to understand the logic behind
the model’s predictions.

While the integration of XAI approaches to improve interpretability is still
an emerging area of research, the application of AI and ML in drug discovery
is quickly progressing. Comprehensive studies that explore the state-of-the-
art in XAI for drug development are severely lacking. It is challenging to
gain a comprehensive knowledge of the state of the field and its potential
because existing research frequently focuses on particular XAI features or
unique drug discovery tasks. Additionally, the majority of AI-driven drug
development research stress predictive performance over interpretability [10],
frequently forgoing the latter in favor of greater accuracy. This imbalance is
critical since regulatory approval and real-world use of AI-driven drug
development methods depend more and more on interpretability. To ensure safe,
effective, and moral medication development, researchers, physicians, and
policymakers need a comprehensive knowledge of how AI algorithms generate
their predictions [11].

The proposed article offers a complete and organized overview of the present
state-of-the-art in XAI for drug discovery, that aims to fill the vacuum in
the existing literature. The article will emphasize the benefits, drawbacks,
and prospective uses of the various XAI approaches used in drug development by
synthesizing and analyzing them. The thorough examination will not only go
over the various XAI techniques but also show how they may be used for various
drug discovery processes, such as target identification, chemical design, and
toxicity prediction. Some of the significant contributions of the article are
as follows:

  * •

XAI and drug discovery methods fundamentals and their significant role in
promoting the healthcare sector.

  * •

Detailed exploration of XAI applications in healthcare, focusing on drug
discovery tasks.

  * •

Evaluation of XAI frameworks’ strengths and limitations in drug discovery
considering performance parameters.

  * •

Critical discussion of lessons learned, limitations, and research challenges
in implementing XAI for drug discovery.

  * •

Identification of future research directions, inspiring innovative approaches
for XAI in drug discovery.

##  2 Background

###  2.1 XAI (What is XAI?)

Since the initial theoretical studies on AI, there have been a large number of
innovative applications using AI in order to help in the development of
society. An AI system must perform decision-making in a reliable, secure way
that can be audited by a user with little technical knowledge. For the user to
understand how the AI makes decisions, techniques are needed that seek to
explain why certain AI input information produces a specific result. To do
this task, XAI is used.

XAI is a technique that aims to logically explain to a user the behavior and
decision-making of an AI system [12]. XAI evolved due to the need to interpret
decisions made by a machine learning model, in this case, XAI indicates the
logic performed by a model to reach conclusions from a classification process.
Therefore, XAI provides corrective measures, error prediction, and explanation
of failures that occur in a system. This allows confidence in the model
results [13]. Fig. 1 shows the comparison of popular XAI models based on the
accuracy and performance metrics of their explainability characteristics. On
the path of marching towards stronger AI from weaker AI, robust XAI models are
in demand for sensitive healthcare applications like cancer treatment, drug
delivery, and drug discovery.

a Explainability vs. Prediction Accuracy

b Explainability vs. Learning Performance

Figure 1: Comparison of popular XAI models based on the accuracy and
performance metrics on explanability

###  2.2 Drug Discovery Methods

Drug discovery is an area that develops drugs that eliminate or minimize
disease. According to Pandiyan and Wang, [14], the average period of research
and development lasts around 10 to 17 years for the development of a new form
of medicine and with an estimated average value of more than US $ 2.8 billion.
Other challenges are quite present in these researches, such as the low
efficiency and high cost of conventional methods to carry out drug discoveries
[15]. So, there is a need to develop new methods that can minimize these
problems. An alternative to minimize these problems in drug discovery can be
through AI. Through AI in drug discovery, applications are possible for
virtual screening, drug design, reaction prediction, protein design, and other
predictive tasks [16].

Singh et al. [17] used multi-scale temporal convolutional networks (MSTCN) to
develop a model based on deep learning named MSTCN-ABPpred (BL) that
classifies antibacterial peptides (ABPs). The main contribution of this study
was the continuous learning capacity that the model has in the face of new
data for re-training.

###  2.3 Search Strategy

The search strategy used in this article to find scientific papers is divided
into three stages. The first step is to come up with terms related to
Explainable AI and Drug Discovery. Therefore, the main search terms were:
“Explainable AI” and “drug discovery”. Other terms were also used, such as
drug development, drug design, drug toxicity, Artificial Intelligence, Machine
Learning and Neural Networks. In the second stage, the search was carried out
in journal articles. Duplicate articles were excluded.

In the third stage, the insertion criteria were: works with the aim of drug
design, reaction prediction, protein design, target identification, compound
design and toxicity prediction through AI, ML and especially XIA. This search
selects works published since 2019 in order to find recent research. Studies
were searched in IEEE Xplore, ScienceDirect, Springer, PLOS ONE, Inderscience,
MDPI, Hindawi, Wiley and peerJ databases. Found xx from IEEE Xplore, xx from
ScienceDirect, xx from Springer, xx from PLOS ONE, xx from Inderscience, xx
from MDPI, xx from Hindawi, xx from Wiley and xx from peerJ.

##  3 XAI in Healthcare

###  3.1 Drug discovery and XAI

In the past 20 years, Computer Aided Drug Discovery (CADD) proved its efficacy
and its significance has been improved in leaps and bounds. In recent years,
many Artificial Intelligence in Drug Discovery (AIDD) strategies were utilized
for drug discovery. Sharma et al. [18] discussed the pros and cons of AIDD in
their survey. At the outset, AI was employed to design logical programming
platforms (LISP, Prolog) at par with usual programming architectures. Later,
as a sub-topic of machine learning (ML), many new techniques in Knowledge Base
Systems (KBS) like Genetic Algorithms (GA), Support Vector Machines (SVM),
Artificial Neural Networks (ANN), Fuzzy Systems (FS), pattern recognition
tools, and Deep Learning (DL) were devised that were utilized in AIDD. The
overlap between the operations performed at the atomic level by molecular
modeling approaches and AIDD-based operations is enhancing with tremendous
growth. There are lots of efficient AI-based methods in drug discovery, but
their applications are limited in both functionality and capability. One of
the major limitations of AI methods such as neural networks is that they are
often regarded as black boxes. As these methods depend on the training
dataset, there is always a concern for generalizing a situation that was not
depicted in the dataset. One of the drawbacks of the genetic algorithm
techniques is that there is no surety of achieving optimal solutions. When the
data size is not large enough, the performance of the deep learning models
deteriorates. Sufficient reliable data is the key to the success of AIDD
models.

Sahoo et al. [19]reviewed how AI can aid in finding the drug-like stuff in the
compound screening phase predicting ADMET and the Structure-Activity
Relationship (SAR) in lead detection and optimization stages, sustainable
development of chemicals in the synthesis stages up to the assistance of AI in
the conduct of repurposing and clinical trials. There is a paradigm shift from
hit and trial approach in drug discovery to traditional drug discovery and
development. To overcome the black-box approach of the AI methods, XAI is the
flag bearer. Machines or robots can compile the data, especially in the
rational drug discovery process so that drug designers plan better and the
synthesis process is easier.

XAI is an area that explains predictions made by AI models. Gillani et al.
[20] defined the value of features while predictions were made. The prediction
of cancer’s reaction to a particular treatment or drug efficiency is a hot
topic. In drug discovery, based on huge genomics data, drug sensitivity
forecasting is a vigorous process. On the other hand, drug personalization is
an arduous and tedious matter. XAI imparts dependency and confidence. Their
research was a step towards the understanding of drug chemical structures and
deep learning strategies on gene expression.

Non-Small Cell Lung Cancer (NSCLC) demonstrates inherent heterogeneity at the
molecular level that helps in discriminating between two of its subtypes -
Lung Squamous Cell Carcinoma (LUSC) and Lung Adenocarcinoma (LUAD). Dwivedi et
al. [21]presented a new XAI-based deep-learning approach to locate a small set
of NSCLC biomarkers. Their framework employed an Autoencoder to reduce the
input feature space and NSCLC instances were classified into LUSC and LUAD
using a feed-forward neural network. They detected that 14 of the biomarkers
are druggable. The survivability of the patients could be predicted by 28
biomarkers. They observed that seven of the newly discovered biomarkers had
never been utilized for NSCLC subtyping and could be promising for the
targeted therapy of lung cancer.

Due to a lack of prior knowledge, the development of XAI techniques poses a
challenge to the quantitative assessment of interpretability. Rao et al. [22]
devised five molecular benchmarks to exploit quantitatively XAI techniques
utilized in Graph Neural Networks (GNN) approaches and compared them with
human experts. The vital substructures for chemists could be identified by the
XAI techniques that delivered informative and reliable explanations and could
be empirically demonstrated.

The cost and time of new drug development can be mitigated as drug
repositioning exhibits great potential. Drug repositioning can confirm the
necessity of pharmacological effects on biomolecules for application to new
diseases by omitting different R & D processes. In a disease-drug association
prediction architecture, biomedical explainability provides insights into
ensuing in-depth studies. Takagi et al. [23] introduced an explainable drug
re-positioning approach called GraphIX utilizing biological networks and
examined its explainability quantitatively. They applied a graph neural
network to learn node features and network weights.

In drug discovery, deep learning can play a significant role if it imparts in
development efforts and experimental research [24]. This will require time and
effort as it needs long drug development times. Medicinal chemists witnessed
state-of-the-art methodological advances via DL in synthesis design but lack
in DL tools in compound optimization. It is a challenging task to transform
expert-dependant DNN architectures into widely usable and robust compound
models. There is a need of narrowing the gap between experiments and DL for
enhancing the confidence of practitioners to generate trust in the
predictions.

Most XAI techniques do not come as readily operational, ‘out-of-the-box’
solutions, which need to be configured for each application [25].
Additionally, deep knowledge of the problem domain is vital to detect which
model decisions demand further explanations, which types of answers are
meaningful to the user, and which are instead expected or trivial. Finding
such types of solutions needs the collaborative effort of biologists,
chemists, data scientists, chemoinformatics, and deep-learning experts to
confirm that XAI methods deliver trustworthy answers and serve the intended
purpose. XAI in drug discovery suffers a dearth of an open-community platform
for enhancing model interpretations, and software and sharing training data.
For federated, decentralized model deployment and secure data handling across
pharmaceutical companies, MELLODDY (Machine Learning Ledger Orchestration for
Drug Discovery, melloddy.eu) is a great initiative. This kind of collaboration
fosters acceptance, validation, and development of XAI.

Karger et al. [26] conducted a bibliometric analysis of AI for drug discovery.
They considered 3884 articles published between 1991 and 2022. They surveyed
the most productive countries, institutions, and funding sponsors in the
domain. They utilized thematic analysis and science mapping to identify the
thematic areas and core topics. They also outlined future research avenues in
the field of drug discovery using AI. The findings indicated the
multidisciplinary nature of AI and its understanding of discovering drugs.
They encouraged the utilization of unsupervised learning algorithms to
identify patterns in unlabelled data to address unknown drug discovery
problems. They considered the explainability of AI algorithms as another
future research need.

Askr et al. [27] performed a systematic Literature review (SLR) that
integrated the current DL strategies along with various types of drug
discovery problems, drug–drug similarity interactions (DDIs), Drug–target
interactions (DTIs), drug-side effect predictions and drug sensitivity and
responsiveness. They are linked with the benchmark databases and data sets.
They also discussed related topics like digital twining (DT) and XAI and how
they support drug discovery issues. Moreover, success stories of drug dosing
optimization were also narrated. They presented open problems for future
research challenges as well.

Real World Data (RWD) and AI showcased their potential but applied in limited
areas across several phases of the drug development process. Most of the AI
studies aimed at the detection of adverse events (AE) from clinical narratives
in Electronic Health Records (EHRs) and a few of them examined clinical drug
repurposing and trial recruitment optimization. The AI techniques on RWD
demonstrated its efficacy by generating novel hypotheses and exploring
previously unknown associations. Nevertheless, challenges and knowledge gaps
still exist, for example, the difficulty of sharing clinical data, data
quality issues, and lack of transportability and interpretability in AI
models. Chen et al. [28] surveyed the latest advancements of AI in drug
discovery and their challenges. Enhancing the capability of DL models that
could handle heterogeneous and longitudinal RWD and new research opportunities
in drug development are some of the areas to exploit.

A huge amount of resources, capital, and time are required to search for
effective treatment of existing and novel diseases. The dearth of
antimicrobial agents for the treatment of emerging infectious diseases like
COVID-19 is a major concern. AI and other in silico techniques can boost the
drug discovery arena by proving more cost-friendly approaches with better
clinical tolerance. Numerous researchers have been working on devising AI
platforms for hit identification, lead optimization, and lead generation. In
[29], Bess et al. investigated the effective AI techniques that revolutionized
the pharmaceutical sciences and drug discovery.

Han et al. [30] described the challenges of XAI in biomedical science. AI
systems should not only have good results but they should yield good
interpretability. Good explainability is missing in the prevailing AI models
in bio-medical sciences and hence hinders creating transparency and
trustworthiness. Reliable results with good interpretations of why it works
rather than only it works is the key and there is an urgent need to design XAI
models in the domain. In biomedical data science, numerous types of data range
from EMRs, bio-image data, text, high-dimensional omics data, and sequencing
data. The complexity, nonlinearity, and size of the data along with problems
mostly disease-oriented force the AI methods to make the trade-off between
good explainability and good performance. The learning biases created by AI
methods are another issue that prevents the techniques from showcasing the
minimum interpretations. The learning biases can be the results of imbalanced
data, wrong parameter setting or tuning, mismatched interactions between a
certain type of data and AI methods, or other complicated problems that may
not be identified by biomedical data scientists. There is remarkable progress
in XAI recently where knowledge-based data representation, learning process
visualization, rule-based learning, and human-centered AI model evaluation
were exploited to improve AI explainability.

By extracting features and relations, performing reasoning, and structuring
information, knowledge graphs play a vital role in XAI for explainability.
Rajabi et al. [31]focused on the role of knowledge graphs in XAI models in
healthcare. Based on their review, they asserted that knowledge graphs in XAI
may be utilized for the detection of adverse drug reactions, drug-drug
interactions, and healthcare misinformation and to mitigate the research gap
between AI-based models and healthcare experts. They also pointed out how to
leverage knowledge graphs in post-model, in-model, and pre-model XAI models in
healthcare to enhance their explainability.

##  4 Impact of XAI on Drug Discovery and Development

###  4.1 AI in Drug Discovery

The field of AI in drug discovery is a rapidly growing and evolving area of
research, with a goal to enhance the efficiency and accuracy of the drug
discovery process. In recent years, AI has been applied in several stages of
drug discovery, including target identification, molecular screening, lead
optimization, and toxicity prediction. One of the key outcomes of AI in drug
discovery is the ability to analyze and process vast amounts of biological
data, including genomic, proteomic, and pharmacological data, to identify
potential drug targets [32]. Additionally, AI algorithms can be used to screen
virtual libraries of chemical compounds to identify potential drug candidates
that fit specific criteria. Another significant outcome is the ability of AI
to predict drug toxicity and safety more accurately compared to traditional
methods [33]. AI models can analyze large amounts of data on chemical
compounds and their interactions with biological systems, providing a more
comprehensive understanding of their potential side effects. Moreover, AI can
also be used to optimize lead compounds to improve their efficacy,
pharmacokinetics, and drug-like properties, allowing for the development of
better drugs with fewer side effects [34].

###  4.2 Role of XAI in Drug Discovery

AI has shown great promise in accelerating the drug discovery process and
improving the efficiency and accuracy of various stages of drug development.
However, further research is required to validate and optimize AI models for
drug discovery, as well as to address ethical and regulatory challenges [35].
XAI is an important area of research in drug discovery that focuses on
developing AI models that can provide clear and transparent explanations for
their predictions and decisions. Fig. 2 shows the schematic representation on
the impact of XAI approaches over drug discovery applications.

Figure 2: Role of XAI in Drug Discovery and other contributions towards Drug
Delivery

In drug discovery, XAI can play a crucial role in ensuring the transparency
and accountability of AI models, particularly in critical decision-making
processes such as lead optimization and toxicity prediction [22]. This can
help to build trust and confidence in the outcomes generated by AI models, and
facilitate the adoption of AI technologies in the pharmaceutical industry. XAI
can also help to identify and mitigate potential biases in AI models, ensuring
that they produce unbiased and fair predictions. This is particularly
important in drug discovery, where biases can have serious consequences,
including the development of ineffective or harmful drugs [25]. Furthermore,
XAI can help to improve the interpretability of AI models, allowing
researchers to understand how AI models are making predictions and to identify
potential flaws or limitations in their algorithms. This can facilitate the
development of more accurate and reliable AI models for drug discovery [36].
Furthermore, it also helps to ensure the development of safe and effective
drugs. Some of the major roles XAI can play and impact to revolutionize the
drug discovery and development process are:

  * •

Data Analysis: XAI algorithms can help in the analysis of large amounts of
complex and diverse data, including chemical, biological, and clinical data,
to identify potential drug targets, predict drug efficacy and toxicity, and
optimize drug designs [37]. Data analysis in XAI drug discovery involves the
use of advanced computational methods and machine learning algorithms to
process and analyze vast amounts of data generated from various sources in the
drug discovery process. This data can include molecular structures,
biochemical assays, and high-throughput screening data, as well as pre-
clinical and clinical trial data.

  * •

Decisions Based on Evidence: Through XAI models we can positively expect
interpretable and transparent reasoning behind their predictions and
decisions, enabling researchers and regulators to better understand and
evaluate the evidence behind the predictions [38]. In drug discovery,
decisions are based on evidence that is generated by AI algorithms. These
algorithms analyze large amounts of data from various sources such as medical
records, scientific publications, and experimental results to identify
potential drug candidates.

  * •

Improved Clinical Trial Design: XAI can help to identify the most appropriate
patient population for clinical trials and improve the design of clinical
trials by predicting the likelihood of success and identifying potential
adverse effects. It is a key aspect of XAI drug discovery, through which they
are used to test the safety and efficacy of new drugs in humans [39]. XAI can
be used to improve the clinical trial design in predictive modeling, patient
selection, and safety measures

  * •

Personalized Medicine: To analyze patient data and predict the response of
individuals to specific treatments, and enable the development of more
personalized and effective treatments XAI algorithms are largely helpful [40].
XAI drug discovery can play an important role in enabling personalized
medicine by using AI algorithms to analyze large amounts of data to support
evidence-based decision-making through drug repurposing and real-time
monitoring [41].

  * •

Improved Regulation: XAI can help regulators to make more informed decisions
by providing interpretable and transparent reasoning behind the predictions
made by AI models, ensuring that drugs are developed and approved safely and
effectively [25]. The goal of improved regulation is to ensure that new drugs
are safe and effective for use in humans, while also promoting innovation in
the drug discovery process. Through risk assessment strategies, this
information can then be used to inform regulatory decisions and ensure that
the new drugs are safe for use in humans.

As XAI has the potential to significantly improve the efficiency, speed, and
accuracy of drug discovery and development, it ultimately leads to the
development of more effective and safer drugs for patients.

####  4.2.1 Transparency and accountability

XAI models can provide clear explanations for their decisions and predictions,
making it possible for researchers to understand how they arrived at their
outcomes. In drug discovery, XAI models can help to address critical issues
such as lead optimization and toxicity prediction [42]. They can provide a
deeper understanding of the underlying biological mechanisms of drugs,
allowing researchers to make informed decisions about their development.

Moreover, XAI models can also help to mitigate potential biases in AI
algorithms, which can have serious consequences in drug discovery, including
the development of ineffective or harmful drugs. This can help to ensure that
AI models produce unbiased and fair predictions, regardless of demographic or
socio-economic factors [43]. Further, XAI models can improve the
interpretability of AI algorithms, allowing researchers to identify potential
limitations or flaws in the algorithms and make improvements. This can lead to
the development of more accurate and reliable AI models for drug discovery
[25]. The development of transparent and accountable XAI models is crucial for
ensuring the development of safe and effective drugs, providing researchers
with a deeper understanding of the underlying biological mechanisms and
improving the accuracy and reliability of AI algorithms in drug discovery
[27].

####  4.2.2 Bias management

Mitigating potential biases in Explainable Artificial Intelligence (XAI)
models for drug discovery is crucial for ensuring that AI algorithms produce
fair, unbiased, and trustworthy predictions. Bias in AI algorithms can have
serious consequences in drug discovery, including the development of
ineffective or harmful drugs [28]. There are several methods that can be used
to mitigate biases in XAI models for drug discovery:

  * •

Data pre-processing: This involves ensuring that the training data used to
develop XAI models is representative of the population and does not contain
any biases [44]. This can be done by using diverse and balanced datasets and
removing any irrelevant or biased features from the data.

  * •

Model selection: Choosing appropriate XAI algorithms that have been shown to
reduce biases is another important step. For example, some algorithms, such as
decision trees and random forests, have been shown to be less prone to biases
compared to other algorithms [38].

  * •

Model interpretation: XAI models that provide clear and transparent
explanations for their predictions and decisions can help to identify
potential biases in the algorithms [27]. This can be done by analyzing the
model’s feature importance or decision paths and comparing them to expert
knowledge.

  * •

Model validation: Evaluating XAI models on independent datasets can help to
identify and mitigate biases in the algorithms. This can be done by comparing
the predictions made by the model with ground truth data and identifying any
discrepancies [38].

  * •

Human oversight: It is crucial in ensuring that XAI models are fair and
unbiased, which can involve domain experts in the development and validation
of XAI models, and regularly review the models’ outcomes to identify and
mitigate any biases [45].

Ultimately, mitigating biases in XAI models for drug discovery is crucial for
ensuring the development of safe and effective drugs. By using a combination
of these methods, it is possible to reduce the potential for biases in XAI
algorithms and to improve the accuracy and reliability of AI models in drug
discovery.

####  4.2.3 Interpretability concerns

Interpretability is one of the key aspects of XAI models for drug discovery,
as it allows researchers to understand how AI algorithms are making
predictions and decisions [25]. This can provide important insights into the
underlying biological mechanisms of drugs and improve the accuracy and
reliability of AI models. There are several methods that can be used to
improve the interpretability of XAI models for drug discovery:

  * •

Model visualization: XAI models that provide visual representations of their
predictions and decisions can help researchers to understand how they are
making predictions [46]. For example, decision trees and rule-based models can
be visualized as a tree or set of rules, respectively, which can provide
insights into the model’s decision-making process.

  * •

Model explanation: XAI models that provide clear and transparent explanations
for their predictions and decisions can help to improve the interpretability
of AI algorithms [47]. This can be done by providing an overview of the
model’s decision-making process or by highlighting the most important features
that influenced the prediction.

  * •

Feature importance: XAI models that provide information about the importance
of individual features in their predictions can help researchers to understand
how the model is using different variables to make decisions [48]. This can be
useful in identifying potential limitations or flaws in the algorithm.

  * •

Model comparison: Comparing different XAI models and their predictions can
help researchers to identify the strengths and weaknesses of each model, and
to understand how they make predictions [49].

  * •

Human oversight: Finally, human oversight is crucial in ensuring the
interpretability of XAI models. This can involve involving domain experts in
the development and validation of XAI models, and regularly reviewing the
models’ outcomes to identify any limitations or flaws [50].

Table 1: Contribution of XAI technology for Drug Discovery and its potential application and treatment. Reference |  Drug type |  XAI Model |  Scheme |  Actuation |  Application |  Treatment
---|---|---|---|---|---|---
[51] |  Small molecule |  XAI |  Bayesian Network |  In silico screening |  Identifying potential leads for SARS-CoV-2 |  COVID-19
[52] |  Protein-based |  ML |  Decision tree |  Target identification |  Predicting drug-protein interactions |  Cancer
[32] |  Small molecule |  DL |  CNN |  Compound optimization |  Predicting compound potency |  Cardiovascular disease
[53] |  RNA-based |  NN |  RNN |  Target prediction |  Identifying drug targets for genetic disorders |  Genetic disorders
[54] |  Protein-based |  XAI rules |  Rule-based |  Target prediction |  Identifying potential drug targets |  Neurological disorders

###  4.3 Developed XAI Modelling Frameworks

There are several frameworks that can be used to develop Explainable
Artificial Intelligence (XAI) models for drug discovery. Some of the most
common frameworks include:

####  4.3.1 Decision Trees

Decision trees are a simple and interpretable XAI framework that can be used
for drug discovery. They work by dividing a dataset into smaller subsets based
on the values of individual features and making predictions based on the
majority class in each subset. Decision trees can be visualized as tree-like
structures, which makes it easy to understand the model’s decision-making
process. The advantage of decision trees is that they are easy to interpret
and understand, making them a good choice for XAI models [55]. They also tend
to be relatively fast to train and can handle both numerical and categorical
data, which makes them well-suited for a variety of applications.

For a drug delivery XAI model, the decision tree could be used to predict the
most appropriate delivery method for a patient based on various patient
characteristics and delivery-related factors [28]. For example, the tree might
consider factors such as patient age, weight, medical history, and the type of
drug being delivered. The tree would split the data based on the values of
these features and determine the best delivery method for each subset of
patients.

However, decision trees are not always the best choice for every XAI problem.
They can be prone to overfitting, especially when dealing with a large number
of features, and they can become very complex, making them difficult to
interpret [56]. In these cases, other machine learning techniques, such as
random forests or gradient boosting, might be a better choice.

####  4.3.2 Rule-Based Models

Rule-based models are another simple and interpretable XAI framework that can
be used for drug discovery. They work by using a set of rules to make
predictions, based on the values of individual features. Rule-based models can
be visualized as a set of rules, which makes it easy to understand the model’s
decision-making process [54].

For a drug delivery XAI model, a rule-based system could be used to determine
the most appropriate delivery method for a patient based on various patient
characteristics and delivery-related factors. For example, the system might
include rules such as "if the patient is elderly and has a history of
respiratory problems, then use a nebulizer for drug delivery," or "if the
patient is younger and has no history of heart problems, then use an
intravenous delivery method." The advantage of rule-based models is that they
are highly interpretable and provide clear and transparent explanations of how
decisions are made [57]. This makes them a good choice for XAI models where
understanding the reasoning behind decisions is important. They are also easy
to develop and maintain, especially for problems where the underlying rules
are well-understood [58].

However, rule-based models can become complex and difficult to manage as the
number of rules grows, and they may not be flexible enough to handle all
possible scenarios [59]. In these cases, other machine learning techniques,
such as decision trees or neural networks, might be a better choice.
Additionally, rule-based models may be limited by the quality of the rules
that are defined, so it is important to carefully consider the knowledge and
expertise of the experts involved in defining the rules.

####  4.3.3 Deep Learning Models

Deep learning models are a more complex XAI framework that can be used for
drug discovery. They work by using a deep neural network to make predictions,
based on the values of individual features [60]. Deep learning models can
provide accurate predictions, but they can be difficult to interpret. For
example, a deep learning XAI model for drug delivery might use a neural
network to predict the most appropriate delivery method for a patient based on
various patient characteristics and delivery-related factors [61]. The model
could then use techniques such as activation maximization or saliency maps to
provide an explanation of how the predictions were made, highlighting which
input features had the most impact on the prediction.

The advantage of deep learning XAI models is that they can handle complex and
non-linear relationships between inputs and outputs, making them well-suited
for problems with a large amount of data and complex relationships [62]. They
can also learn patterns and relationships in the data that may not be
immediately apparent to humans. However, developing XAI models that are both
accurate and interpretable can be a challenge. There is often a trade-off
between accuracy and interpretability, and it can be difficult to balance the
two. Additionally, deep learning models can be difficult to fine-tune and
optimize, and a large number of parameters can make them more prone to
overfitting the data.

####  4.3.4 Hybrid Models

Hybrid models are a combination of multiple XAI frameworks and can be used to
combine the strengths of different models. For example, a hybrid model might
use a deep neural network to make predictions, but also provide a decision
tree as an explanation for the predictions [63]. The advantage of hybrid XAI
models is that they can provide a balance between the interpretability and
accuracy of the individual models, making it easier for humans to understand
how decisions are made. They can also handle complex and non-linear
relationships between inputs and outputs, making them well-suited for problems
with a large amount of data and complex relationships [64].

For example, a hybrid XAI model might combine a rule-based system with a deep
learning model. The rule-based system would provide an initial set of
predictions based on expert knowledge or experience, and the deep learning
model would then refine these predictions based on the relationships it has
learned from the data [65]. The hybrid model could then provide an explanation
of its predictions by combining the explanations provided by the rule-based
system and the deep learning model. However, developing hybrid XAI models can
be more complex than developing individual models, and careful consideration
must be given to how the different models will interact and complement each
other. Additionally, there may be a trade-off between interpretability and
accuracy, and it can be difficult to balance the two.

Apart from the aforementioned models, there are several XAI frameworks that
can be used for drug discovery, each with its own strengths and weaknesses.
The choice of the framework will depend on the specific requirements of each
drug discovery project, including the size and complexity of the dataset, the
interpretability of the model, and the accuracy of the predictions.

##  5 Open research challenges and Future Directions

In the pursuit of advancing XAI within the realm of drug discovery and
development, there lie several intriguing avenues that invite exploration.
This section delves into the multifaceted landscape of challenges yet to be
conquered and promising directions that impress researchers, practitioners,
and stakeholders alike. From addressing the challenges of interpretable model
complexity to promoting uncharted ethical dimensions, these open challenges
and future directions reshape the way we comprehend, utilize, and ultimately
transform the field of drug discovery.

###  5.1 Open research challenges

####  5.1.1 Interpretable Model Complexity

Developing XAI models that can successfully explain the predictions of
sophisticated machine learning models, such as deep learning models, which are
frequently employed in drug discovery. Model complexity and interpretability
still need to be balanced. It is crucial to provide explanations that dissect
the complex decision-making of these advanced models since drug discovery
entails complex biological interactions and enormous datasets [9]. To achieve
this balance, novel methods must be developed that transform the complicated
models’ high-dimensional interactions into understandable insights, enabling
researchers and subject-matter experts to understand and believe AI-driven
forecasts [66].

####  5.1.2 Quantifiable Explanation Metrics

Defining and standardizing metrics to tally the effectiveness of XAI-provided
justifications. This would make it possible to evaluate these explanations’
dependability and informational value objectively. It is crucial to establish
reliable measures that measure explanations’ ability to reveal innovative
insights as well as their alignment with domain knowledge. The credibility and
comparability of various XAI techniques will also be improved by creating a
unified framework for evaluating these metrics across various drug discovery
domains, making it easier to choose and optimize the best techniques for
particular research contexts [67, 68].

####  5.1.3 Multi-Modal Data Integration

Constructing XAI models that can logically combine and explain predictions
drawn from a variety of data sources, including clinical data, omics data, and
molecular structures. It is difficult to ensure consistent and comprehensible
explanations across these many data kinds. The need for XAI approaches that
can integrate knowledge from genomes, proteomics, chemical structures, and
patient profiles is critical given the large quantity of data that goes into
modern drug discovery [69]. This necessitates not just a knowledge of complex
machine-learning techniques but also the capacity to translate these insights
into understandable justifications that physicians and researchers from other
fields can rely on and act upon. The creation of adaptive XAI frameworks that
integrate these various sources into a unified story may hasten the
identification of new therapeutic approaches in this multidimensional world
[70].

####  5.1.4 Trustworthiness and Robustness

XAI explanations must be reliable and resilient, particularly when facing
adversarial attacks or noisy data. It is essential to create tools that can
recognize and counteract false or biased claims. The vulnerability of XAI
systems to adversarial manipulation and uncertainty in real-world data creates
a significant problem in the dynamic environment of drug development, where
trustworthy conclusions are crucial. The confidence of researchers and doctors
will increase by fostering a new paradigm of XAI that not only clarifies the
AI’s reasoning but also protects against potential distortions [71]. The
foundation for informed decision-making will be strengthened by working toward
explanations that are not only understandable but also resilient in the face
of varied data complexities and possibly malevolent impacts, propelling the
ethical and efficient use of AI in the process [72].

####  5.1.5 Human-Computer Interaction

Designing user-friendly user interfaces that successfully communicate XAI-
generated explanations to domain experts in drug discovery, allowing them to
base their decisions on these explanations. It’s difficult to strike the ideal
balance between information richness and simplicity. Constructing user-
friendly interfaces that convert complex model outputs into useful insights is
a multifaceted task as XAI emerges as a bridge between complex machine
insights and human cognition. This covers both the cognitive psychology
involved in communicating complicated information as well as the aesthetics of
visualization [73]. A critical step toward realizing the transformative
potential of AI within the dynamic domain is making sure that these interfaces
cater to a variety of user backgrounds, enabling researchers, clinicians, and
decision-makers to gain crucial insights from AI-driven predictions while
maintaining the depth of technical understanding [74].

###  5.2 Future research directions

####  5.2.1 Dynamic Explanation Adaptation

Looking into methods for dynamically changing XAI explanations as models
change and new data streams in. This study might produce explanations that
continue to be correct, pertinent, and in line with how drug development is
evolving [75]. The insights provided to researchers and clinicians can
maintain their interpretability and reliability by using adaptable XAI
frameworks that automatically update explanations in response to model updates
or changes in data distribution [76]. This will enable informed decision-
making even in the face of changing complexity.

####  5.2.2 Ethical and Societal Implications

Exploring the ethical dimensions of XAI in drug development, such as questions
of prejudice, justice, openness, and data privacy. It will be essential for
the ethical implementation of AI-driven healthcare solutions to comprehend and
handle these ethical issues. We can proactively build systems to eliminate
biases, maintain openness, and safeguard patient data, creating trust and
responsibility within the healthcare ecosystem, by investigating the ethical
implications of utilizing AI to drive important decisions in drug development
[77, 78].

####  5.2.3 Hybrid Models and Fusion Strategies

Study of hybrid models, which combine the benefits of comprehensible methods
with the propensity for prognosis of complex models. Creating fusion tactics
that seamlessly combine these methods could result in explanations that are
precise and comprehensible. We can combine the benefits of sophisticated
machine learning and interpretable models to develop a new class of hybrid
models that provide predictions with high accuracy and accompanying insights
that are simple to understand, facilitating sound decision-making [9, 78].

####  5.2.4 Longitudinal Data and Temporal Insights

XAI techniques that can explain predictions based on patient longitudinal
data, capturing illness development and treatment responses across time, are
the focus of this study. Personalized treatment plans might undergo a
revolution if interpretable insights into temporal dynamics are made possible
[79]. XAI techniques that can understand the complex temporal relationships
within these data streams will enable clinicians to identify crucial disease
milestones, evaluate treatment efficacy, and design interventions that adapt
to the changing state of patient health as healthcare data becomes more
longitudinal and dynamic [28].

####  5.2.5 Domain-Specific Explanation Languages

Designing domain-specific languages or frameworks that make it easier for
multiple stakeholders, like as regulators, patients, and regulatory agencies,
to translate complex AI-generated explanations into insights they can use
[80]. We can bridge the gap between technical AI outputs and practical
decision-making by adapting the language of explanations to the unique needs
and backgrounds of varied users [81]. This will allow stakeholders at all
levels to effectively utilize the potential of AI-driven insights in drug
discovery.

##  6 Conclusions

The landscape of drug discovery has undergone a profound shift with the
integration of AI and ML. As these technologies advance, the demand for
transparency and interpretability has become increasingly paramount. XAI is a
groundbreaking approach that tackles this very concern by offering a
comprehensible insight into the outcomes produced by intricate ML models. The
presented review article has presented a comprehensive spotlight on the
current status of XAI within drug discovery. Encompassing a range of XAI
methods and their application in this domain, the article underscores both the
potential and the challenges of implementing XAI techniques. The examination
extends to XAI’s role in diverse drug discovery facets, such as target
identification, compound design, and toxicity prediction. With a forward-
looking perspective, the article also contemplates potential research
trajectories, envisioning how XAI could further reshape the landscape of drug
discovery. Ultimately, this review serves as a valuable compass, guiding us
through the evolving terrain of XAI’s transformative impact on the field of
drug discovery.

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