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Chapter 17
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Articial Intelligence inDrug Discovery
andDevelopment
GeethaaSahgal andJeevandranSundarasekar
Abstract Recently, machine learning (ML) and articial intelligence (AI) have
gained popularity in the healthcare sector. In the eld of drug discovery and development, numerous strategies have been developed to create a human healthcare system
that is benecial. Many automated drug development design pipelines and validated
ML algorithms are currently being imagined for various diseases. Preclinical research
is a crucial stage in the drug development process before ML predictive models are
used. These pipelines facilitate a better understanding of the diseases and related biological. These methods enable the exploration of previously unsolvable problems,
such as property prediction, molecular design, and synthetic route optimisation, while
also lowering the expense and duration of research associated with nding a new drug
through invitro or invivo models. An overview of the application of AI and ML to
drug development is given in this chapter. It talks about the various applications of
these technologies as well as the issues that still need to be resolved. An examination
of AI and ML’s potential applications in drug discovery rounds out the chapter.
Keywords Articial intelligence · Machine learning · Drug discovery and
development · Future directions and challenges
17.1 Introduction
Drug discovery and development is the process of identifying and developing a
novel medication with particular chemical properties to treat a particular disease.
Precision and trial-and-error drugs are the two types of approaches currently used
G. Sahgal (*)
Faculty of Pharmacy, AIMST University, Bedong, Kedah, Malaysia
e-mail: geethaa@aimst.edu.my
J. Sundarasekar
Faculty of Applied Science, AIMST University, Bedong, Kedah, Malaysia
e-mail: jeevan@aimst.edu.my
Ltd. 2024
S. Bose et al. (eds.), Concepts in Pharmaceutical Biotechnology and Drug
Development, Interdisciplinary Biotechnological Advances,
https://doi.org/10.1007/978-981-97-1148-2_17
363© The Author(s), under exclusive license to Springer Nature Singapore Pte
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Fig. 17.1 The phases in the drug development process (Réda etal. 2020)
G. Sahgal and J. Sundarasekar
in drug discovery and development. Precision medicine, according to Collins and
Varmus (2015), emphasises the unique variations in each person’s genes, environment, and lifestyle (Collins and Varmus 2015). It can be more challenging to
develop new methodologies for this approach because it differs from the traditional “one-size-ts-all” approach to treating diseases. On the other hand, precision medicine might lead to more successful and customised patient care
(Carracedo- Reboredo et al. 2021). Trial-and-error medicine is another common
strategy in drug discovery. This approach often necessitates testing multiple medications on patients before identifying the most effective one, which can make it
challenging and expensive. Trial-and-error medicine can, nevertheless, remain a
valuable instrument in the drug discovery process since it can be used to identify
novel, promising therapeutic candidates. The production of pharmaceuticals has
long been subject to a regulatory framework that safeguards the quality of completed goods. This is a complex and labour-intensive process that has traditionally
relied on labour-intensive techniques like high-throughput screenings and trialand-error invitro and invivo studies (Patel and Shah 2022). With this process, it
can take over 10years to get approval and treat a patient successfully. The whole
process is described in Fig.17.1. According to estimates from the Tufts Centre for
the Study of Drug Development (CSDD), it would have cost roughly $2.6 billion
as of 2014 to develop a new prescription medicine that is approved for sale (Deep
Pharma 2020). Additionally, as shown in Fig.17.2, the article stated that it takes
overcoming multiple stages of obstacles before a single novel targeted drug is
found (Réda etal. 2020).
17 Articial Intelligence inDrug Discovery andDevelopment
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Fig. 17.2 Pharma efciency: challenges (Deep Pharma 2020)
17.2 Revolution ofArticial Intelligence (AI)
inDrug Discovery
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In recent years, a large amount of biomedical and digitisation data has been produced
by small individual laboratories and large international initiatives (CarracedoReboredo etal. 2021). The amount of data in the pharmaceutical industry is increasing
dramatically, which makes it difcult to collect, analyse, and apply that knowledge to
complex clinical issues (Paul etal. 2021; Ramesh etal. 2004). AI was therefore created to handle large amounts of data with better automation. AI is a technological
system that mimics human intelligence in learning and problem- solving through a
range of state-of-the-art tools and networks. AI is capable of interpreting input data,
learning from it, and making decisions on its own to achieve specic goals. The
employment of software and systems in the healthcare sector surely does not endanger people’s ability to exist in person (Paul etal. 2021; Yang and Siau 2018; Wirtz
etal. 2019). The advancement of this technology for use in the medical and pharmaceutical industries by experts will signicantly improve patient care.
More data is being produced by the pharmaceutical industry than ever before, rendering outdated traditional data storage techniques. There is an enormous surge in drug
candidates as we enter the big data (BD) era of contemporary drug discovery.
Consequently, AI aids in the management and analysis of collective information through
deep learning and relevant modelling studies (Paul etal. 2021; Solanki etal. 2022). The
rst attempts to use the drug discovery process in the pharmaceutical industry were
funded by Merck and were known as the 2012 QSAR ML.This complex model signicantly increases the predictivity of the developed drug candidates for the toxicity and the
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G. Sahgal and J. Sundarasekar
adsorption, distribution, metabolism, and excretion (ADME) data sets (Zhu 2019). This
opened the door for the development of neural network techniques, which the pharmaceutical industry currently uses extensively for drug discovery and development.
The pharmaceutical industry employs 240 AI companies, 600 investors, and 90
corporations, as per the AI for Drug Discovery, Biomarker Development, and
Advanced R&D Landscape, 2020 report (Deep Pharma 2020). However, as of
2022, 495 AI companies, 1120 investors, and 100 corporations are involved in the
pharmaceutical industry, according to the AI for Drug Discovery Landscape
Overview landscape Q1 teaser report. According to Deep Pharma Intelligence
2020, the United States (US), China, the United Kingdom (UK), Canada, the
European Union (EU), Asia, and Australia contributed 54.4%, 2.5%, 14.2%,
6.7%, 13.4%, 8.4%, and 0.4% of AI for drug discovery in 2020 (Deep Pharma
2020). On the other hand, according to Deep Pharma Intelligence 2022, there will
be increases in the US, China, EU, Asia, and the Middle East of 55.15%, 3.83%,
16.76%, and 9.89%, respectively (Deep Pharma 2022). When considering AI
from a global perspective, the US leads the AI industry in terms of the number of
businesses that use AI to drive R&D, research centres, institutes, and investments.
Novartis is the major driver of increased AI activity in the pharmaceutical industry
in the UK and EU.Additionally, AstraZeneca and BenevolentAI work together on
innovative AI-generated targets for chronic kidney disease. China has made signicant investments in AI totalling $5 billion. Tianjin, one of the largest cities in
China, is investing $16 billion in the region’s AIsector. In addition, Beijing will
construct a $2.12 billion AI research project. China aims to lead the world in AI
by 2030, according to the AI Strategic Plan, which was published in July 2017.
According to Deep Pharma Intelligence’s 2022 analysis, Saama Technologies,
Inc., a prominent clinical data analytics company, is one of the driving forces
behind the implementation of AI in the Asia-Pacic region. This demonstrates the
rapid growth of AI in the pharmaceutical sector for global drug development
(Deep Pharma 2022).
Figure 17.3 shows the market drug discovery timeline for AI.Just a small number of businesses decided to start the pilot collaborations and make modest investments in AI drug discovery and development during the rst part of the timeline.
Due to the failure of numerous pilot projects in the life sciences, deep learning for
drug discovery and advanced R&D was heavily criticised during the second phase.
Nevertheless, there is an intense rivalry to acquire the best AI startups, and technology testing gets underway. The AI industry’s capitalisation continued to rise in the
fth year of the market timeline, seemingly justifying many of the early investors’
stakes. The big pharma industry is engaged in erce competition to prioritise AI and
related technologies in drug development platforms. As a result, numerous AI startups partnered with major pharmaceutical companies, including AstraZeneca, Pzer,
Novartis, Takeda, Merck, and Jansen (Réda etal. 2020). The top pharma companies
by number of pharma AI deals and the Top 10 AI and Technology Partners by number of signicant pharma AI deals in 2021 and Q1 2022, respectively, are displayed
in Figs.17.4 and 17.5. The AI and Pharma partnership from February 2022 to the
end of 2021 is outlined in Table17.1 (Réda etal. 2020).
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Fig. 17.3 AI for drug
discovery market timeline
(Deep Pharma 2022)
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Fig. 17.4 The top pharmaceutical companies based on the quantity of deals in pharma AI between
2021 and Q1 2022 (Deep Pharma 2022)
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Fig. 17.5 Based on the quantity of signicant pharmaceutical AI deals in 2021–Q1 2022, the to
10 AI and technology partners (Deep Pharma 2022)
Table 17.1 The collaboration between AI and pharma industry from the 2021 to February 2022
(Réda etal. 2020)
Month/
Year Collaborators Description
January
2021
February
2021
March
2021
April 2021 AstraZeneca,
May 2021 Bristo Myers Squibb
June 2021 GSK and Progentec GSK and the AI startup Progentec have announced an
June 2021 Teva and Insilico
July 2021 Eli Lilly and Verge
August
2021
Merck and Philips To developments in customised reproductive treatment
based on AI
Roche and PatchAi Together with PatchAi, Roche Italia is launching a virtual
platform for cancer patients
Pzer and IKTOS Pzer’s small-molecule initiatives will be relevant
AI-powered de novo design software from Iktos
University of Florida,
and NVIDIA
AstraZeneca is collaborating on new AI research projects
and Exscientia
medicine
genomics
Insilico medicine and
Westlake Pharma
with NVIDIA and the University of Florida with the goal
of improving patient care and drug discovery
18-month collaborative research agreement
To make use of Insilico’s ML technology, Teva and
Insilico medicine partnered
Eli Lilly and Verge genomics collaborated to use AI in
drug development to produce novel medications for the
treatment of amyotrophic lateral sclerosis
Westlake Pharma and Insilico medicine have announced a
collaboration to expedite the development of novel
coronavirus drugs
G. Sahgal and J. Sundarasekar
17 Articial Intelligence inDrug Discovery andDevelopment
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Table 17.1 (continued)
Month/
Year Collaborators Description
September
2021
October
2021
December
2021
January
2022
January
2022
January
2022
February
2022
Summit
pharmaceuticals
international and
CytoReason
Poolbeg pharma and
Eurons genomics
Roche and Genentech Recursion’s platform will be utilised by Genentech and
Merck and AbSci In a $610 million deal, AbSci and Merck teamed to
Sano and Exscientia Exscientia and Sano worked together to create novel
Amgen and generate
biomedicines
Bayer, Aalto and HUS The cooperation between HUS, Aalto, and Bayer was
The goal of the partnership between CytoReason and
summit pharmaceuticals international (SPI) is to
introduce CytoReason’s ML platform to the Japanese
clinical drug discovery market
Poolbeg pharma and Eurons genomics launch an AI
programme
Roche to nd new drugs in the elds of oncology and
neurobiology
produce enzymes using AbSci’s AI platform
therapeutic candidates for immune-mediated illnesses and
cancer
Amgen and generate biomedicines worked together to
develop protein therapies for ve different clinical targets.
Amgen might be required to pay up to $1.9 billion in
exchange for this partnership in order to obtain a
cutting-edge AI-driven platform
extended to use AI to support clinical drug trials
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17.3 The Computational Methods Used by AI Companies
The rst stages towards the gradual advancement of AI in drug discovery are topological descriptors, molecular ngerprints, QSAR modelling, ML strategies
(k- nearest neighbours, random forest, support vector machines, and genetic algorithms), and modelling techniques (simulated annealing and genetic algorithms)
(Zhu 2019). Wen and colleagues report on a deep learning (DL) model designed to
predict interactions between drugs and their biological target, based on 15,524
drug-target combinations extracted from the DrugBank database (Zhu 2019; Wen
etal. 2017). ML, symbolic AI, deep learning (DL), evolutionary algorithms, reinforcement learning, convolutional neural networks (CNN), cheminformatics, bioinformatics, natural language processing (NLP), quantum computing, GANs, and
federated learning are some of the computational techniques used by the most
advanced AI companies in the pharmaceutical industries (Deep Pharma 2022). The
technologies and computational techniques employed by AI companies are compiled in Table17.2 (Deep Pharma 2022).
ML, a branch of AI, uses big datasets and algorithms to train and adapt to simulate how the human brain works. ML needs to be appropriately adjusted throughout
the process to utilise any kind of data. For the algorithm to learn a model with available parameters through training data, a particular model must rst dene the necessary parameters (Dara etal. 2022). This training data model will be used to predict
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Table 17.2 The most cutting-edge AI businesses employ these computational techniques and
technologies (Deep Pharma 2022)
Computational techniques
Company
Ardigen Bioinformatics, deep
Atomwise ML, DL, CNN,
BenchSci NLP, DL, ML Decodes data from both open and closed sources
BenevolentAI ML, DL, symbolic AI,
Berg ML, DL, bioinformatics Create new biomarkers and treatment targets by
Berkeley lights Ml, bioinformatics Automate cell selection, processing, and analysis.
BioSymetrics NLP, DL, ML Manage raw drug data sets, phenotypic data,
BIOZ NLP, DL, ML For the benet of the life sciences community,
Bioxcel
therapeutics
C4X discovery ML, DL,
CelerisTx DL, bioinformatics It is a deep learning company that validates lead
employed Technologies synopsis
In addition to developing custom applications,
learning, NLP
chemoinformatics
chemoinformatics
ML, DL, chemoinformatics Leading the way in fusing pharmacological
chemoinformatics,
bioinformatics
Ardigen also works in the areas of BD integration,
biological and clinical data analysis, and
laboratory information management systems
In addition to developing custom applications,
Ardigen also works in the areas of BD integration,
biological and clinical data analysis, and
laboratory information management systems
about reagents, like antibodies, and provides
published gures with useful information
Decodes data from both open and closed sources
about reagents, like antibodies, and provides
published gures with useful information
analysing information derived from patient
samples in both state of healthy and illness
Enables scientists to: Automate the production of
cellular therapies and hasten the development of
cell lines
imaging data, and genomics data. Allows for the
rapid integration of analytics and ML capabilities
into ongoing commercial procedures by
researchers
bioz has developed a search engine that can sift
across the vast majority of webpages containing
intricate, unorganised scientic publications using
ML and natural language processing
knowledge combined with AI and BD analytics is
the biopharmaceutical business Bioxcel
corporation
Using human genetic datasets, C4X’s novel
DNA-based target identication platform
(Taxonomy3(R)) nds new targets that are
specic to individual patients
drug candidates in automated lab settings and
breaks down unbreakable targets using creative,
computer-based techniques
G. Sahgal and J. Sundarasekar
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