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17 Articial Intelligence inDrug Discovery andDevelopment
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The biotech platform business Arctoris makes use of its completely automated
drug discovery platform. It operates out of Singapore, Boston, and Oxford. An
oncologist and a medicinal/synthetic chemist founded the company intending to
utilise technology to its fullest potential and combine it with a wealth of industry
knowledge to expedite the search and development of novel therapies. The company’s fundamental tenet is that without richer, more consistent, and repeatable underlying data, drug discovery programmes cannot advance and hit the next milestone
more quickly and with a higher probability of success. The most signicant obstacle
to using AI and ML for drug discovery is obtaining reliable, repeatable, wellstructured, and heavily annotated data. Arctoris can generate massive amounts of
ML-ready data thanks to robotics, which expedites the process from target to hit,
lead, and candidate and improves decision-making. The combination of precision
robotics, a specialised data science platform, and the seasoned knowledge of biotech and pharma veterans in drug discovery enables both quality and speed. Arctoris
fully logs and analyses every experimental output, including temperature, humidity,
CO2, batch ID, provenance of the reagent, and a plethora of other metadata.
Furthermore, the platform facilitates automated quality assurance and quality control, employing statistical methods to ensure the total validity and dependability of
each outcome. Arctoris ensures that superior data will be generated in a faster manner, enabling better decisions to be made earlier in both human-powered and, more
specically, AI/ML-driven programmes. This is because AI models are trained
using the best available data. Combining data science and robotics, Arctoris has
produced a cutting-edge technology platform that powers drug discovery projects
both inside the company and through partnerships with international biotech and
pharmaceutical companies (Réda etal. 2020). Numerous factors are included in the
industry standard for data generation and processing, including a sparse set of highlevel results data, a highly fragmented le and storage system, vague reagent and
cell line provenance, inconsistent application of techniques and protocols, variability, and human error. On the other hand, Arcotoris-enabled data generation and processing offers precise adherence to automated protocols, completely validated
reagents and cell lines with comprehensive audit trails, repeatable results data in a
standard format, extra rich research metadata collection, safe and helpful data storage and access, and sophisticated assay performance monitoring.
A genomics startup called Genomenon uses AI to organise the world’s genomic
knowledge in order to speed up the identication of genetic diseases and the creation of treatments. Offering a comprehensive understanding of the genetic drivers
and clinical features of any genetic disease, the ProdigyTM Genomic Landscapes
from Genomenon support the entire drug development process from discovery to
commercialisation. Gene disorders, inherited illnesses, and somatic and hereditary
cancers are the main therapeutic areas of Genomenon. Genomenon’s ProdigyTM
Genomic Landscapes, which use a special blend of professional, scientic review
and internal genomic language processing (GLP), offer an evidence-based basis for
every phase of the drug development process. A novel technique called GLP is used
to systematically extract and standardise genomic and clinical data from scientic
and medical literature. GLP is more sensitive than traditional techniques at
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identifying complex genomic information, which will lead to the identication of
more variants and patients. Genomenon’s database, which was created using GLP,
now has more than 14.8 million variants, 8.8 million full-text articles, and 3 million
supplemental datasets. Genomenon and Alexion, the Rare Disease division of
AstraZeneca, collaborated to use AI technology to speed up the genetic diagnosis
process for patients with uncommon diseases. With regard to ATP7B, a gene associated with Wilson disease, Genomenon’s AI-powered approach discovered 3.7 times
as many pathogenic/likely pathogenic variants (869 pathogenic variants) with
evidence- supported results as the crowdsourced ClinVar database (235 pathogenic
variants). In order to help doctors make more accurate diagnoses, this greatly
increases the tools at their disposal.
The AI startup GATC Health speeds up the process of nding and developing
new drugs. GATC Health, an AI startup, speeds up discovering and creating new
medications. The company provides highly efcient services to pharmaceutical
companies that reduce risk in the drug discovery process. GATC Health develops
a cutting-edge AI-based drug development platform from beginning to end. The
platform supports in silico clinical trial simulation, drug and therapeutic solution
development, early disease detection, identication of disease biology, and
invitro and invivo testing feedback loop simulation. To identify and validate new
drugs, GATC’s Platform replicates human biology using highly detailed diseasespecic data and exclusive AI solutions. The company develops an innovative
approach to drug discovery that boosts productivity and dramatically shortens the
time to clinical development. Oncology, neurology, cardiology, immunology,
rheumatology, and other diseases are among those that GATC Health targets.
Through preclinical drug de-risking, drug compound discovery, and diagnostic
biomarker discovery, GATC Health employs AI in research and development. To
gain insight into the disease, AI tools are utilised to determine the causal relationship between the biomarkers and the illness. A set of distinctive medicinal compounds is produced with the help of AI-assisted compound discovery. The
identied diagnostic biomarkers’ causal and effect impacts were evaluated mathematically. The veried causal biomarkers and pathways are simulated and evaluated using AI-assisted database models in conjunction with human expertise. This
will lead to a nal set of treatment targets. Six to nine months is GATC Health’s
drug discovery timeline.
G. Sahgal and J. Sundarasekar
17.6 Challenges ofUsing AI inDrug Discovery
Despite the potential benets of AI in drug discovery, there are many challenges
and limitations to consider. One of the primary challenges is the availability of
relevant data (Vamathevan et al. 2019). AI-based techniques typically require
large data sets to be trained (Tsuji etal. 2021). The precision and reliability of the
results may be impacted by the fact that there is frequently a limit to the amount
of data that is available, or the data may be inconsistent or of low quality
17 Articial Intelligence inDrug Discovery andDevelopment
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(Blanco-Gonzalez etal. 2023). A problem with AI-based solutions is that they can
raise ethical concerns about bias and fairness. For example, if the training data is
biassed or unrepresentative, the predictions produced by an ML algorithm may be
unfair or inaccurate (Silvia and Carr 2019). One of the most important things to
consider is making sure AI is applied fairly and ethically when creating new therapeutic compounds. Numerous strategies and techniques can be employed to
address the obstacles AI encounters in the eld of chemical medicine. One tactic
is data augmentation, which entails producing articial data to improve datasets
that already exist. According to Grabner etal. (2021), contemporary AI methods
are insufcient to replace traditional experimental protocols or the expertise and
experience of human investigators (Grebner et al. 2021). AI can only forecast
what it can infer from available data; human scientists will then need to conrm
and analyse the results (Gilpin etal. 2018). On the other hand, merging AI with
traditional experimental methods can also help the process of discovering new
medications. Combining AI’s predictive capability with human researchers’
expertise and knowledge can expedite the development of new drugs and optimise
the drug discovery process (Wang etal. 2019). The biopharma industry still faces
a signicant challenge from the global lack of talent in AI.However, it is expected
that in the coming years, an increasing number of specialised university courses
and programmes centred on data science and AI applications will at least partially
address this problem (Réda etal. 2020).
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17.7 Summary ofthePotential AI forRevolutionising
Drug Discovery
AI holds the potential to revolutionise the drug discovery process by enhancing
speed and accuracy, producing more personalised and effective treatments, and
speeding up drug development. However, for AI to be successfully applied in drug
discovery, high-quality data must be available, ethical concerns must be resolved,
and the limitations of AI-based techniques must be understood. Promising strategies
for overcoming the difculties and constraints of AI in the context of drug discovery
are provided by recent developments in the eld, such as the use of explainable AI,
data augmentation, and integration of AI with conventional experimental methods.
This is an intriguing and promising area of research that has the potential to drastically alter the drug discovery process, especially in light of the increasing interest
and attention from academics, pharmaceutical companies, and regulatory bodies, as
well as the possible advantages of AI.AI in the pharmaceutical sector will enable
the creation of drugs that are individually customised to meet the requirements of
every patient, including the right dosage, release parameters, and other components.
By highlighting and advancing the signicance of mechanisation, using the current
AI-assisted algorithms should shorten the time it takes for the goods to reach the
market, improve their quality, and increase the overall security of the manufacturing
scheme. Better resource and cost efciency should follow from it as well.
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G. Sahgal and J. Sundarasekar
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Chapter 18
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AI: Catalyst forDrug Discovery
andDevelopment
KhushbuNailwal, SumitDurgapal, KhushbooDasauni,
andTapanKumarNailwal
Abstract This abstract explores the multifaceted roles of AI in expediting the drug
development process. AI-driven algorithms analyze vast datasets, unraveling complex biological interactions and identifying potential drug targets with unprecedented speed and precision. In silico drug screening, powered by machine learning
models, accelerates the identication of promising compounds, minimizing the
time and resources traditionally required. Furthermore, AI facilitates personalized
medicine by analyzing individual patient data to tailor treatments based on genetic
and molecular proles. In clinical trials, AI optimizes patient recruitment, enhances
trial design, and expedites data analysis, leading to more efcient and cost-effective
drug development. While the adoption of AI presents unprecedented opportunities,
challenges such as data security, interpretability, and ethical considerations must be
navigated. This chapter provides a comprehensive overview of the current state of
AI in drug discovery, emphasizing its potential to reshape the pharmaceutical industry and improve global healthcare outcomes.
Keywords AI · Drug discovery · Drug development · Machine learning
18.1 Introduction
In the span of the past 200years, advancements in modern medicine have signicantly enhanced the lives of innumerable patients. Illnesses and ailments that were
once considered untreatable or lethal have been vanquished with the aid of therapeutic drugs developed to extend and enhance the quality of life. The rise in new
diseases and the resurgence of once-controlled illnesses, often in altered forms, are
K. Nailwal · S. Durgapal
Department of Pharmaceutical Sciences, Kumaun University, Nainital, India
K. Dasauni · T. K. Nailwal (*)
Department of Biotechnology, Kumaun University, Nainital, India
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_18
387© The Author(s), under exclusive license to Springer Nature Singapore Pte
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K. Nailwal et al.
attributed to the changes in the environment, lifestyle changes, food contamination,
the widespread use of antibiotics and chemicals in farming, and the growing problem of drug-resistant pathogens. So, accelerated drug discovery and development
are required across diverse therapeutic areas of the healthcare system to address
these growing unmet medical needs (Fig.18.1).
Drug discovery is the process by which new medications or pharmaceutical compounds are identied, designed, and developed for the treatment of diseases and
medical conditions. The drug discovery and development process consists of several steps, including identifying drug targets, validating those targets, transforming
initial hits into leads, rening the leads, determining preclinical molecules, evaluating them preclinically, conducting clinical trials, and obtaining regulatory approval
(Sarkar etal. 2023). Pharmaceutical companies primarily aim to bring new drugs to
market by navigating the intricate stages of drug discovery and development.
However, this process of creating a new drug is both time-consuming and expensive, requiring a diverse range of technologies and expertise. Generally, it takes
approximately 15years and an average investment of $2.8 billion to discover and
develop a new drug (Qureshi etal. 2023). The challenges posed by conventional
approaches in drug discovery, such as limited effectiveness and signicant expenses,
have become signicant obstacles. Consequently, it has become necessary to
explore novel methods to address the time-consuming and costly nature of this
endeavor (Chen etal. 2022). With the revolutionizing research and developments in
the areas of high-performance hardware, cloud prowess, and availability of a high
volume of classied genomic, proteomic, pharmacological, clinical trial, and omics
data, articial intelligence and machine learning had paved the way for drug discovery and development (Singh etal. 2023). Machine learning (ML) algorithms, especially deep learning (DL) algorithms, have energized the drug discovery process.
Recently, deep learning methods, such as articial neural networks (ANNs) with
Fig. 18.1 Number of compounds vs time for drug development; the number of compounds represents the chemical compounds being scanned to identify a drug candidate
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multiple hidden layers, have experienced a resurgence. They are gaining attention
for their ability to automatically extract meaningful information from input data and
capture complex relationships between inputs and outputs. These capabilities
enhance traditional machine learning techniques, which rely on manually engineered molecular descriptors. The initial skepticism surrounding the usefulness of
AI in pharmaceutical discovery is gradually fading away, opening up exciting possibilities for the eld of medicinal chemistry. The integration of AI and advanced
experimental insights is anticipated to transform the quest for innovative and
enhanced pharmaceuticals. It promises to make the process faster, more costeffective, and increasingly compelling. Deep learning-enabled methods are beginning to tackle critical challenges in drug discovery. Moreover, numerous
technological advancements, such as “message-passing paradigms,” “spatialsymmetry- preserving networks,” “hybrid de novo programming,” and other innovative machine learning approaches, are poised to become widely adopted (Sarkar
etal. 2023). These methods will play a pivotal role in unraveling some of the most
profound and intriguing questions in the eld.
This chapter establishes a fundamental groundwork for a variety of machine
learning principles and techniques. It subsequently delves into their application at
various points within the drug discovery and development process.
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18.2 Machine Learning inDrug Discovery andDevelopment
Machine learning is an area of articial intelligence (Fig.18.2) involving the development of algorithms and statistical models that allow computer systems to infer the
trends and patterns in existing data without being explicitly programmed; these
models and data can subsequently be employed for making forecasts on fresh information (Vilar and Costanzi 2012).
18.2.1 Types ofMachine Learning
Categorized on the basis of the learning approach, we have the following classication of ML algorithms (Fig.18.3).
Supervised Learning
This type is based on learning from labeled training data, which includes inputoutput pairs. The algorithm learns the relationship between the input features and
the output labels, with the goal of predicting the output for new, unseen data.
Supervised learning is used for tasks such as classication, regression, and multilabel classication. In drug discovery, QSAR models are used to predict the
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Fig. 18.2 Visual representation of AI as a superset
Fig. 18.3 Main types of ML
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biological activity of potential drug candidates based on their chemical structures
(Vilar and Costanzi 2012). Using a dataset of known chemical structures and their
corresponding biological activities, the supervised learning algorithm learns to correlate molecular features with the desired biological effects. This helps in the identication of promising drug candidates for further testing and optimization (Vora
etal. 2023).
Unsupervised Learning
In this type, the algorithm is provided with an unlabeled dataset and must nd patterns, relationships, or structures within the data without prior knowledge of the
desired output. Unsupervised learning is suitable for tasks such as clustering, anomaly detection, and dimensionality reduction. Molecular clustering is an unsupervised learning technique used to group similar compounds together based on their
structural or physicochemical properties (Kovács etal. 2005).
Semi-supervised Learning
This category incorporates both annotated and unannotated data during the training process. The algorithm can use the labeled data to guide its learning process
while also discovering patterns in the unlabeled data. Semi-supervised learning is
useful when labeled data are limited or expensive to obtain. In the drug discovery
process, it is used to group similar compounds together based on their structural
or physicochemical properties. This can help researchers identify the relationships between various compounds and their biological activities, leading to the
discovery of new chemical scaffolds and drug candidates. For example, hierarchical clustering or self-organizing maps (SOM) can be applied to analyze large
compound libraries and identify groups of molecules with similar properties
(Takács etal. 2021).
Reinforcement Learning
Reinforcement learning algorithms learn through interaction and feedback, aiming
to maximize cumulative rewards. They are used in robotics, gaming, and resource
allocation and can improve prediction accuracy in drug discovery tasks. In de novo
drug design, reinforcement learning guides the generation of novel chemical structures with desired properties based on feedback from a scoring function. This can
lead to discovering new drug candidates more efciently (Popova etal. 2018).
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