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17 Articial Intelligence inDrug Discovery andDevelopment
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Table 17.2
Company
CytoReason ML, DL, symbolic AI,
Data4Cure ML, DL, NLP A variety of heterogeneous data types can be
Deep genomics ML, DL, bioinformatics Deep genomics is using AI to open up a new
Desktop
genetics
Envisagenics ML, DL, high-performance
Euretos ML, DL, bioinformatics Through an intuitive application, Euretos offers
Exscientia ML, DL, bioinformatics,
Genialis ML, DL Genialis is a platform that combines AI-based
GNS
healthcare
Healx ML, NLP, symbolic AI,
(continued)
Computational techniques
employed Technologies synopsis
Cutting-edge ML technologies and unrivalled
chemoinformatics,
bioinformatics
Bioinformatics, ML Motivated by the practical applications of
computing
chemoinformatics
ML, DL Through the integration and conversion of
chemoinformatics,
bioinformatics
access to both proprietary and public data enable
CytoReason to create proprietary biological
models of disease, tissue, and drug
integrated and subjected to advanced analysis by
distinct system components, including both
structured and unstructured molecular,
phenotypic, and clinical data, thanks to the
Data4Cure platform’s modular architecture
world of potentially life-saving genetic therapies
CRISPR technology, a team of genome editing
specialists, bioinformaticians, and data scientists
established desktop genetics. The biggest
database on genome editing in the world was used
to train DESKGEN AI, their primary technology
The SpliceCore platform from Envisagenics
combines high-performance computing,
RNA-splicing analytics, and proprietary ML
algorithms to nd disease-specic alternatively
spliced RNA that may be used as a target for
therapy
easy access to the cloud-based platform for
discovery and facilitates the safe integration of
public and proprietary company data
The business predicts a molecule’s pharmacology,
novelty, synthetic accessibility, and ADME using
ML
techniques with computational biology to model
and combine data at the nexus of clinical and
translational medicine
numerous patient data formats into in silico
patients, GNS healthcare AI technology reveals
the intricate network of interactions underlying
the progression of an illness and how well a
treatment works
By integrating and transforming a wide range of
patient data types into in silico patients, GNS
healthcare AI technology reveals the intricate
network of interactions underlying the course of
disease and the effectiveness of treatments
(continued)
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G. Sahgal and J. Sundarasekar
Table 17.2
Company
Iktos ML, DL, cheminformatics Iktos is creating a ligand-based de novo drug
Insilico
medicine
Kyndi NLP, DL, ML Leading AI software is offered by Kyndi, which
MEdChemica ML, chemoinformatics The company’s goal is to accelerate our clients’
Nference NLP, DL Modern neural networks are used by nferX to
NuMedii BD analytics, DL, ML Analyse millions of raw human, biological,
Nuritas DL, bioinformatics Estimate the therapeutic potential of bioactive
Owkin ML, federated learning Owkin advances precision medicine by fusing his
Peptone DL (TensorFlow + Keras
Phenomic AI DL, reinforcement learning Phenomics nds drugs that specically target
ProteinQure Quantum computing,
(continued)
Computational techniques
employed Technologies synopsis
design technology based on deep learning (DL),
with an emphasis on multi-parametric
optimisation (MPO)
DL, GANs GANs +
reinforcement learning,
symbolic AI, ML,
chemoinformatics,
bioinformatics
base)
reinforcement learning,
chemoinformatics
All-inclusive DL pipeline. Biology: HTS analysis,
DNNs for target ID, and signalling pathways.
Chemistry: Generating novel molecules with
GANs-RL
can analyse lengthy texts and produce insights
that can be put into action more quickly,
intelligently, and understandably
initiatives through its extensive knowledge in
method development, lead optimisation, and lead
generation
automate real-time knowledge extraction from
scientic, commercial, and regulatory literature
pharmacological, and clinical data points to nd
connections between medications and diseases at
the systems level
peptides derived from food. Permits the
following: The economical development of highly
focused treatments for particular diseases using
natural food sources
knowledge of biology and ML.Owkin’s data
connect service makes real-world data more
accessible by therapeutic area
The rst protein database in the world with
complete KerasTM and TensorowTM integration
designed especially for deep learning and AI
applications
these resistant cells and makes predictions about
which cells will withstand chemotherapy.
Subsequently, the compounds will be developed
and commercialised
To create protein medications, ProteinQure is
integrating atomistic simulations, quantum
computing, and reinforcement learning. Without
crystal structures, they can investigate protein
structures and create peptide-based medicines
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Table 17.2
Company
ReverieLabs Evolutionary algorithms, MLStructure-based ML predictive models for
ReviveMed ML, DL With the help of ReviveMed’s platform, metabolic
Structura
biotechnology
Xralpi Quantum physics, ML With XtalPi’s ID4 platform, it is possible to
(continued)
Computational techniques
employed Technologies synopsis
small-molecule potency and ADMET/PK
characteristics
data can be applied quickly, efciently, and
economically to identify novel disease
mechanisms for drug discovery. Metabolomic
biomarkers can also be used to determine which
patients would benet from treating the
underlying disease mechanism
ML (stochastic gradient
descent and branch-andbound maximum
likelihood optimisation)
The cryoSPARC SystemTM uses machine
learning (ML) to enable nding protein and
molecular complex structures in high throughput
using cryo-EM data
accurately predict factors related to
physiochemical and pharmaceutical aspects of
solid-form selection, small-molecule candidates
for drug design, and other important areas of drug
development
the test data based on the required parameters. A few of the industries currently
using ML (ML) in drug discovery and development are nding materials, consumables, chemical synthesis, data mining (ontology), biology research (target validation), lead discovery, preclinical development, biomarker discovery, clinical
development, pharmacovigilance, drug repurposing, and virtual lab assistants (Dara
etal. 2022). ML principles offer numerous opportunities for target identication,
peptide synthesis, drug toxicity and physiochemical property evaluation, drug monitoring, drug efcacy and effectiveness, and drug repositioning when developing a
new medication, all made possible by a chemical library that holds over 106 million
compounds (Gupta etal. 2021).
The process of developing a targeted medicine must include lead optimisation,
target identication and validation, hit recovery, and clinical trials (Vohora and
Singh 2018). Physicians have been able to create meaningful targeted medications
by using computer-assisted drug design (CADD), an articial technology. This
method produces the lead compounds with optimal properties in silico and provides
theoretical molecular properties for the targeted medicine, such as distribution,
absorption, metabolism, excretion, selectivity, and bioactivity (Dara etal. 2022).
Preclinical drug development and discovery costs eventually decrease with the use
of multi-objective ML techniques. The pharmaceutical industry has therefore been
more willing to spend money on ML technologies, instruments for getting exact
results, and targeted drugs for drug development.
The utilisation of BD facilitates the collection of pharmaceutical research ndings,
which are then transformed into studies involving the application of various algorithms
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and reusable techniques. The drug discovery process makes extensive use of a wide
range of ML tools, such as support vector machine (SVM), random forest, Naive
Bayesian classication (NBC), multiple linear regression (MLR), logistic regression
(LR), linear discriminant analysis (LDA), probabilistic neural networks (PNN), multilayer perceptron (MLP), and multiple linear regression (MLR) (Dara etal. 2022). Due
to the lack of a standardised digital mechanisation method or universal software for
computer monitoring of chemical reactions, a variety of AI-aided computational tools
are available to aid in drug discovery, such as Alphafold, Chemputer, Conv_qsar_fast,
Chemical VAE, DeepChem, DeepNeuralNet-QSAR, Deep Tox, Delta Vina, Hit Dextar,
QML, SIEVE-Score, and many more (Sarkar etal. 2023).
G. Sahgal and J. Sundarasekar
17.4 Role ofAI Tools inDrug Discovery
Drug design, drug repurposing, drug polypharmacology, and drug screening are the
four main facets of drug discovery. The potential of AI to forecast drug characteristics may mean that repeated clinical trials involving human subjects are not necessary for ongoing research. This would be benecial both in terms of cost and ethics.
ML has two subsets: supervised and unsupervised learning. While the clustering
model is an example of unsupervised learning, classication and regression analysis
are examples of supervised learning (Dara etal. 2022). The clustering method nds
and groups similar data points in larger datasets without regard to the exact result.
The Hidden Markov model, Hierarchical clustering, K-means, and Neural Networks
are the clustering models used in de novo molecular design, deep feature selection
for biomarkers, feature reduction in single-cell data to identify cell types, and cell
types of biomarkers from single-cell RNA data (Dara et al. 2022). Neighbour,
SVMs, NLP Kernel Techniques, and NLP Bayesian classiers are models that are
used in classication. These models are used to study tissue-specic biomarkers
from gene expression signatures, target disease drug associations, and target drug
ability based on PK properties, protein structure or sequence, and protein structure
(Dara etal. 2022). Models for regression analysis include linear regression, spare
linear regression, random forests, and decision trees. These models can identify
molecular features that predict drug responses to cancer, targets for diseases such as
Huntington’s disease, target drug ability from multi-dimensional data, and gene
expression data that predict the success of clinical trials (Dara etal. 2022).
17.5 Research andDevelopment (R&D) Approaches ofAI
inDrug Discovery
Biology and computer sciences applied mathematics differ in bioinformatics is
gradually closing thanks to many emerging collaborations between academic institutions, research facilities, pharmaceutical labs, and AI and ML businesses. This
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would allow for the completion of drug development pipelines more quickly because
they could be done computationally, automatically, and with a reduced chance of
human error. The time it takes to nd possible drug candidates could be reduced
from several months to a year by using AI tools (Réda etal. 2020). It is advisable to
proceed with caution and refrain from assuming that the failure rate problem can be
entirely resolved through computational methods. However, it might also be able to
shorten the duration and expense of drug development while simultaneously making therapies more patient-centred by integrating ML techniques into pipelines.
This is due to the possibility that applying or enhancing precision medicine techniques may be made simpler by incorporating multi-view data. However, systematic
approaches make it easier for studies to be replicated and reused, for data quality to
be transparently and consistently controlled and shared, and for promising targets to
be identied in silico. Furthermore, before conducting any preclinical or wet lab
testing, these methods may provide numerical values for comparing and assessing
the efcacy of possible compounds (Réda etal. 2020).
Nowadays, a large number of biopharmaceutical companies, including AbbVie,
AstraZeneca, Amgen, BenevolentAI, Insilico Medicine, Inc., Sano, Recursion
Pharmaceuticals, Owkin, Strados Labs, Antiverse, Standigm, and Deep Genomics,
use AI in their research and development. The most inventive AI research and development in biopharma is compiled in Table17.3. The mission of AbbVie, a multinational biopharmaceutical company focused on research, is to develop and
commercialise innovative therapies for some of the most difcult and dangerous
illnesses by leveraging its expertise, dedicated workforce, and innovative approaches.
Alzheimer’s, schizophrenia, and Parkinson’s disease are AbbVie’s main focus areas.
AI has been used to increase patient adherence to medication, visually conrm medication ingestion, and use data to evaluate treatment efcacy. This business collaborates with BenchSci, Atomwise, AiCure, Calico Labs, and Mission Therapeutics.
AstraZeneca’s primary focus areas are the development, discovery, and commercialisation of prescription medications, mainly for the treatment of illnesses in the
oncology, cardiovascular, renal and metabolism, and respiratory therapy areas. The
company collaborates with Schrodinger, Mila, Melloddy, MLPDS Consortium,
BenevolentAI, Roivant Sciences, and Tencent to create knowledge graphs that combine genomic, disease, drug, and safety data to develop an understanding of the
disease. It also identies new targets for novel medicines and provides quick and
accurate image analysis (Réda etal. 2020). AI systems are trained to facilitate easier
and more accurate sample analysis for pathologists. The analysis time could be
reduced by more than 30% as a result of this. For one of their AI systems, they
developed a method based on how some autonomous cars interpret their environment. In order to help inform decisions regarding immunotherapy-based bladder
cancer treatment, the AI system was trained to evaluate tumour and immune cells
for the PD-L1 biomarker (Réda etal. 2020).
A new breed of data-driven drug discovery rms that integrate automation and
data science has also emerged as a result of the growth of robotics-focused businesses in the biomedical research sector. Arcotoris, Genomenon, and GATC Health
represent three of the newest waves of data-driven pharmaceutical companies.
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agile development methodologies, the team created a
strong algorithm that can reasonably mimic the
manual process
The company is the creator of the cutting-edge
experimental and computational discovery platform
known as the benevolent PlatformTM, which aids
scientists in developing novel treatments for diseases
and customising drugs for each patient. The three
primary focal points for the benevolent PlatformTM
are precision medicine, molecular design, and target
identication
G. Sahgal and J. Sundarasekar
Using a new AI system called GENTRL (generative
Tensorial reinforcement learning), it has created a
drug discovery process that can be completed in a
matter of days rather than years (rst synthesis and
trials taking only 21days instead of 3years). As a
result of Insilico’s successful testing of the technology
in the rst experimental validation of this a type of AI
technology for nding drugs in animals and cells,
several brand-new compounds that can treat
conditions like brosis were created
Owkin Using natural language processing (NLP) tools and
assessments; to offer individualised
patient care; to assist adherence with
precise and consistent real-time
• To increase the precision of risk
Nu Biopharma Focus area Involvement of AI Collaboration companies Findings
Table 17.3 Summary of the most innovative research and development for AI in biopharma (Source: Deep Pharma Intelligence, 2022) (Deep Pharma 2022)
1 Amgen Cardiovascular
responses; to leverage data to identify the
and
osteoporosis
Neuropore therapies, Novartis,
and AstraZeneca
most efcient course of action
stratication; in the collection of more
varied data; in determining specic
medication targets
• In molecular design; in patient
diseases,
Parkinson’s
disease, cancer
and brosis
2 BenevolentAI Kidney
BioTime, juvenescence AI
limited, Bitfury and Fosun
Pharma
quickly and cheaply
currently used medications
the targets. To predict clinical trial
• To identify drug molecules more
• To identify novel biological targets for
Cancer,
immunity,
kidney disease,
medicine, Inc
3 Insilico
outcomes
• To use innovative chemistry to validate
brosis and
inammatory
bowel disease
(IBD)
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device that measures more than ten distinct health
indicators rapidly and precisely
(2) LIFEdata is an easy-to-use, intuitive AI platform
that automates customised dialogue experiences
across all platforms
(3) In real-world clinical trials, CART can provide
continuous vital sign monitoring and be worn
comfortably for everyday use
(4) To help physicians make better clinical decisions,
NeuroAdvise is a mobile application that provides
clinical decision support
(5) Mentalab is a wearable patch that measures ECG
biosignals continuously to monitor and diagnose
respiratory and cardiac conditions
(6) ChatbotPack.com can assess and diagnose
respiratory conditions and early warning indicators of
neurological illnesses in the elderly
(7) Wavy assistant continuously monitors heart health
in real time using voice and AI technologies
GSK and DNDi (1) MouthLab is a solitary, non-invasive AI-powered
377
(continued)
decentralise clinical trials; to enable
remote (eHealth) communication
between physicians and patients; to
• To diagnose illnesses early; to
Nu Biopharma Focus area Involvement of AI Collaboration companies Findings
4 Sano Central nervous
enhance marketing tactics
system, heart
diseases,
cancer,
diabetes, and
pneumonia
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(1) Sophisticated tools for biotechnology, such as
CRISPR genome editing and synthetic biology
(2) Reliable automation of complex laboratory
research on a never-before-seen scale through the use
of cutting-edge robotics
(3) Neural network architectures for analysing and
drawing conclusions from large, intricate internal
datasets in an iterative manner
(4) Increasing the elasticity of high-performance
computing by utilising cloud solutions
G. Sahgal and J. Sundarasekar
development and has catalogued 30 live disease
models. These models are constructed using
interpreted AI, which sets them apart from
conventional black box models. This enables the
business to advance its research and pinpoint the
biomarkers that underpin its predictions. To nd new
biological targets, optimise clinical trial designs with
• Owkin loop, Owkin studio, and Owkin connect
patient subgroups, and nd patients qualied for
specic treatments, new multimodal biomarkers must
be discovered
are the models
Nu Biopharma Focus area Involvement of AI Collaboration companies Findings
Table 17.3 (continued)
Bayer and Roche It focused on:
synchronised network used for chemical
and biological data design, execution,
aggregation, and storage
high-dimensional dataset covering
various data modalities related to biology
and chemistry
map was created to handle and interpret
• The infrastructure layer is a highly
Genetic
5 Recursion
• Recursion data universe is a
diseases,
infectious
diseases,
inammation
and
pharmaceuticals
immunology
data from the recursion data universe
• The software programme recursion
Roche Owkin AI models: Owkin has 40 more models in
by looking into the mechanisms that lead
to varying drug efcacy from patient to
patient
medication candidates will yield the best
• To better the drug development process
immune system
and
6 Owkin Oncology,
results and enhance treatment
• To determine which patient-specic
cardiovascular
diseases
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device that collects lung sound acoustics wirelessly
and hands-free. This makes it perfect for clinical
studies where the goal is to measure how patients
respond to new medications by collecting coughs
and other lung sounds in an efcient and objective
way. The data collected can then be processed and
genomics workbench mines, processes, and identies
novel targets from thousands of potential targets
(2) Using their platform, the deep genomics team
evaluated over 69 billion oligonucleotide molecules
against a million targets in silico as part of project
Saturn. The result was a library of 1000 compounds
whose ability to manipulate cell biology was
conrmed through experimentation
NIL (1) To nd the best therapeutic candidates, the deep
• The RESP biosensor is the only FDA-approved
• Decentralised trial
compatibility: Clinpal,
thread, icon, Iqvia, Curavit,
Curebase, Medidata
analysed
Bellus, GSK, Merck,
Algernon
• Cough trial solution: Bayer,
biomarkers: Veeva,
• Digital acoustics
Medidata, Datatrak,
PerkinELmer
Paraxel, Pzer, Iqvia, icon,
PPD, Amgen, Labcorp,
Genentech, ERT, Bristol
• Trial partner solution:
379
(continued)
Myers squib
avenue, nd mutations that cause
disease, and gure out how to solve the
genetic issue
• Target discovery: They look into every
Nu Biopharma Focus area Involvement of AI Collaboration companies Findings
7 Deep genomics Genetic
diseases, CNS
likely-to-be-effective targeted therapies,
• Therapy design: To identify the most
diseases, and
metabolic
diseases
the AI evaluates hundreds of thousands
to millions of possible options. After
that, they are conrmed in the wet lab
viability, and genome-wide off-target
effects
• To generate data on animal toxicity, cell
• The respiratory management solution is
8 Strados lab Respiratory
an innovative approach that combines
ML, patient centricity, and lung
biomarkers
signicant advantages from Strados labs’
improvement of pulmonary care
monitoring capabilities, which allow for
• The life sciences sector stands to gain
management
solution
the analysis of longitudinal lung
acoustics to provide insight into patient
drug response
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(1) Within 2weeks, Standigm ASKTM offers unique
targets that are precisely tailored to a customer’s
research context
(2) Standigm’s optimised workow articial
intelligence system can generate multiple rst-in-class
Hanmi, KIST, SAMJIN, SK
chemicals
compounds in less than 7months
(3) Standigm has developed customised partnership
models, from providing AI solutions to licencing AI
assets and platforms, that are perfect for every
customer’s needs
(4) Standigm possesses a remarkable inventory of
ready-made internal therapeutic assets that are both
appealing and able to satisfy customers’ pipeline
requirements
G. Sahgal and J. Sundarasekar
it keeps diversity from being lost during amplication
(2) To extract additional data from the workloads that
are already in place, next-generation sequencing, or
NGS, is employed
(3) Utilising trained models and AI-enhanced drug
discovery, the platform analyses data gathered from
thousands of experiments. To choose the best
candidates, these outputs are compared to known data
NIL (1) In order to nd more rare and diverse antibodies,
offers unique targets that are precisely
• Within 2weeks, Standigm ASKTM
Parkinson’s
Nu Biopharma Focus area Involvement of AI Collaboration companies Findings
Table 17.3 (continued)
9 Standigm Cancer,
tailored to a customer’s research context
optimised AI system can produce
multiple rst-in-class compounds
• Within 7months, this workow-
disease,
non-alcoholic
steatohepatitis
(NASH),
mitochondrial
disease
there are too many options for
antibody-antigen binding, testing can be
concentrated on safely
binders that traditional methods might
overlook
• Offers focused options so that, once
10 Antiverse New type
• Can assist inlocating enough potential
antibody
discovery
will be adequate for clients’ needs
• Can create new binder variations that
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