The worldwide machine
learning in drug discovery market is experiencing significant
expansion, with projections indicating a revenue increase reaching several
hundred million dollars by the end of the forecast period, spanning 2025 to
2034. This growth is driven by emerging trends and strong demand across key
sectors.
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A wide range of factors contribute to the
market expansion, are the need for rapid, more effective, and affordable drug
development approaches, with rising cases of chronic diseases, including
cardiovascular diseases, genetic issues, and neurological conditions.
Furthermore, accelerating advancements in technologies, such as AI integration
and its automation, and also, rising investments and funding in drug discovery
processes with novel advancements.
Machine Learning in Drug Discovery
Market Highlights:
➢ North America
dominated the market in 2024.
➢
Asia Pacific is expected to grow at the fastest CAGR
during 2025-2034.
➢
By application stage, the lead optimization segment
held a major revenue share of the market in 2024.
➢
By application stage, the clinical trial design &
recruitment segment is expected to grow rapidly during the forecast period.
➢
By algorithm type, the supervised learning segment led
the machine learning in drug discovery market in 2024.
➢
By algorithm type, the deep learning segment is
expected to register a rapid expansion in the coming years.
➢
By deployment mode, the cloud-based segment dominated
the market in 2024.
➢
By deployment mode, the hybrid deployment segment is
expected to be the fastest-growing during 2025-2034.
➢ By therapeutic area, the oncology segment held the biggest revenue
share of the machine learning in drug discovery market in 2024.
➢
By therapeutic area, the neurological disorders segment
is expected to grow at a rapid CAGR in the predicted timeframe.
➢
By end user, the pharmaceutical companies segment was
dominant in the market in 2024.
➢
By end user, the AI-focused startups segment is
expected to grow fastest during 2025-2034.
Market Overview
The global machine learning in drug
discovery market is transformed by its ability to examine huge datasets,
detection of patterns, estimate drug characteristics, and escalate clinical
trials. In drug
discovery, machine learning is playing a major role by determining
possible drug candidates, enhancing their properties, and boosting the
prediction of drug efficacy, toxicity, by reducing the lengthy and expensive
drug development process. Moreover, techniques including generative adversarial
networks (GANs) and variational autoencoders (VAEs) are employed to develop
novel de novo drug design with specific features.
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Broder Applications of Machine Learning:
Major Potential
Machine learning has various applications
in the drug discovery process, consisting of models to cater virtual screening
to detect robust drug candidates that are likely to bind to a target protein.
As well as in the case of drug repurposing, ML helps to analyze existing drugs
and their linked data to find their probable new applications with accelerating
the development of new therapies. Besides this, in biomarker detection, ML is
used to diagnose diseases and anticipate treatment solutions.
Model Interpretability and Specialized
Professionals: Major Limitations
The global machine learning in drug
discovery market is facing important challenges, are difficulties in
understanding their decision-making processes through the utilization of their
“black boxes”. As well as nowadays, pharmaceutical
companies require specialized professionals in both data science and
molecular biology/chemistry is creating another barrier in the market.
The Machine Learning in Drug Discovery
Market: Regional Analysis
In 2024, North America dominated the
market, due to its widespread
emphasis on personalized medicine is propelling the demand for AI and
ML-driven approaches to analyze genomic data and patient-specific information
to boost drug development. As well as primarily influencing factors are
advanced technological infrastructure, robust pharmaceutical and biotechnology
areas with major R&D investments.
Whereas, the US is a major country in North
America, which generated its dominance in the market, especially strong and
significant AI companies collaborations with research institutions, and
pharmaceutical giants are thriving in their respective market growth.
For instance,
• In July 2025,
Healx, an expert in AI-powered drug discovery for rare and neglected
conditions, allied with SCI Ventures to introduce solutions for paralysis with
AI-powered drug discovery.
Although Canada is also experiencing
continuous advancements in AI techniques, such as deep learning, ML, and
natural language processing, in the expansion of capabilities of drug discovery
through ML use. Whereas, increasing investments and funding in ML-driven drug
discovery results are boosting startups and well-developed pharmaceutical
companies collaborating to leverage AI capabilities.
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The Asia Pacific is Predicted to be The
Fastest-Growing Region in the Studied Years
Across the globe, Asia Pacific is estimated
to show rapid growth during 2025-2034 in the machine learning in drug discovery
market. Due to the increasing geriatric population, which is highly susceptible
to chronic diseases, are a rising demand for novel and highly efficient drugs,
coupled with technologies like ML. Furthermore, ASAP is widely emphasizing the
adoption of rapid approaches with cost-effectiveness, in which AI and ML-based
drug discovery development is playing a crucial role, with diminishing lengthy
and costly methods.
From ASAP, major countries like China,
Japan, South Korea, and Singapore are actively investing in AI and machine
learning for healthcare innovation, encouraging an ecosystem for AI-driven
drug discovery. However, China possesses vast and varied
biological datasets, such as genomic data are mainly fostering demand for AI
models and the development of personalized medicine approaches.
Generally, the machine learning in drug
discovery market in India, the rising
digitalization of healthcare records, and robust IT infrastructure are
enabling the integration of AI solutions in drug discovery domains. As well as
India is a major hub of pharmaceutical and biotechnology companies with a
raised focus on the development of novel therapies, precision medicines
employed in growing chronic diseases by coupling with AI and ML tools to
enhance efficacy, safety, and affordable candidates.
For this market,
• In July 2025,
XtalPi, an AI-powered drug R&D company, made a strategic collaboration with
Pfizer to expand its AI-driven drug discovery and materials science
simulations.
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The Machine Learning in Drug Discovery
Market: Segmentation Analysis
By application stage analysis
The lead optimization segment dominated the
market in 2024. The segment is driven by the application of AI and ML tools in
target selectivity, biological activity, potency, and toxicity potential. As
well as pharmacophore studies, molecular dynamics, QSAR, and molecular docking
are highly employed in this segment, with the use of machine learning features.
On the other hand, the clinical trial
design & recruitment segment will grow rapidly, by introducing ML
algorithms in electronic health records (EHRs), patient registries, and
other data sources to detect potential particular eligibility
criteria for clinical trials. Also, automated processes can assist in
identifying and pre-screening potential participants, where ML can primarily
minimize the time and resources required for patient recruitment.
By algorithm type analysis
The supervised learning segment held a
major share of the machine learning in drug discovery market in 2024. Inclusion
of many advantages, such as prediction of drug-target interactions, detection
of drug potency, classification of drug candidates, with ADMET anticipation are
fueling the segment growth.
Whereas, the deep learning segment is
estimated to grow fastest, due to significant benefits in the reduction of
process flows and inexpensive approaches. As well as many applications in drug
discovery, like drug–target interactions (DTIs), drug–drug similarity
interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect
predictions. Broad application in growing capabilities in structure-based
predictions and alpha-fold use in protein modeling.
By deployment mode analysis
The cloud-based segment led the machine
learning in drug discovery market in 2024. The segment is fueled by the
accelerating volume of biological and clinical data as well as in healthcare,
including genomic sequences, clinical trial results, and electronic health
records, the growing spending on traditional drug discovery, and advancements
in AI technologies like machine learning and deep learning.
However, the hybrid deployment segment will
show rapid expansion because of the need for integrated capabilities of
on-premise and cloud-based solutions to leverage the computational power of the
cloud while maintaining sensitive data on secure, on-premise systems in
different companies. Along with this, the raised demand for measurable models
with private data integration is propelling the segment growth.
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By therapeutic area analysis
In 2024, the oncology segment led the
machine learning in drug discovery market, with a rising number of cancer cases
fueling demand for novel and targeted
therapies in cancer. Also, machine learning supports in identification
of inhibitors of EGFR and estimates their efficiency in several cancer types.
Also, ML assists in generating anti-angiogenic drugs that target VEGF.
Whereas, the neurological disorders segment
is predicted to grow at the fastest CAGR in the upcoming years. The segment
expansion is impelled by the need for acceleration in the process of CNS drug
discovery, with a better success rate, as well as major influence of AI and
machine learning on drug discovery in the areas of neurodevelopmental
disorders, depression, PD, AD, anesthesia, and pain treatment.
By end-user analysis
Primarily, the pharmaceutical
companies segment held a major share of the machine learning in drug discovery
market in 2024. As these developing and major companies are widely emphasizing
the development of novel drugs and therapies required in generating new
diseases, with enhanced focus emergence of personalized medicine based on
patient data by incorporating sophisticated tools and technologies and boosted
collaborations with technology companies, is driving demand for adoption of ML
in pharma companies.
On the other hand, the AI-focused
startups segment will grow rapidly, by focusing on raising demand for quick,
more affordable drug development, with enhanced cases of conditions such as
cancer, in which startups are grasping AI and ML to detect possible drug
targets, improvements in drug candidates, and escalate clinical trials. With an
aim at VC-backed innovation and fast prototyping, these startups are propelling
themselves to boost drug development approaches.
Machine Learning in Drug Discovery
Market Companies:
• Insilico Medicine
• Exscientia
• Atomwise
• BenevolentAI
• Schrödinger, Inc.
• Relay Therapeutics
• BioXcel Therapeutics
• Cyclica (acquired by Recursion)
• Deep Genomics
• Recursion Pharmaceuticals
• NVIDIA Clara Discovery
• Valo Health
• Aria Pharmaceuticals
• Owkin
• Healx
• Peptone
• Cloud Pharmaceuticals
• Verseon
• XtalPi
• Euretos
Major Progressing Moves by Top Companies
|
Global Player |
Recent Updates |
|
Insilico Medicine (February 2025) |
Partnered with Harbour BioMed to boost AI-driven antibody |
|
Recursion Pharmaceuticals (February 2025) |
Announced significant clinical data on lead AI-based drug |
|
Recursion Pharmaceuticals (October 2024) |
Collaborated with Google Cloud to support Recursion’s drug |
|
Insilico Medicine (October 2024) |
Acquired a major step in AI-powered drug discovery by |
|
Exscientia (August 2024) |
Agreed with Recursion to develop a global technology-enabled drug |
What are the Drifts in the Machine Learning in Drug Discovery Market?
• In April 2025, the Japanese biotech
Prism BioLab collaborated with Elix, an AI drug discovery company, to expand the
development of small-molecule therapies for difficult-to-treat diseases.
• In April 2025, the Icahn School of
Medicine at Mount Sinai launched the AI Small Molecule Drug Discovery Center to
empower artificial intelligence (AI) to revolutionize drug development.
vIn October 2024, Accenture invested
through Accenture Ventures in 1910 Genetics, a biotechnology company to
achieve advancements in small and large molecule drug discovery with a
multimodal AI platform powered by laboratory automation.
Machine Learning in Drug Discovery
Market Segmentation
By Application Stage
• Target Identification & Validation
• Gene Expression
Analysis
• Protein-Protein
Interaction Mapping
• Pathway
Discovery
• Hit Identification & Lead Generation
• Virtual
Screening
• De Novo Drug
Design
• High-Content
Screening
• Lead Optimization
• SAR Modeling
• ADMET
Prediction
• Compound
Prioritization
• Preclinical Development
• Toxicity
Prediction
• Bioavailability & Metabolism
Modeling
• Formulation Selection
• Clinical Trial Design & Recruitment
• Patient
Stratification
• Trial Outcome
Prediction
• Adaptive Trial
Modeling
By Learning Type
• Supervised Learning
• Classification
Models
• Regression
Models
• Unsupervised Learning
• Clustering
• Dimensionality
Reduction
• Reinforcement Learning
• Molecule
Optimization
• Adaptive Design
Systems
• Deep Learning
• Convolutional
Neural Networks (CNNs)
• Recurrent
Neural Networks (RNNs)
• Transformer Models (e.g., AlphaFold
applications)
• Transfer Learning / Federated Learning
• Cross-trial
Predictions
• Secure
Collaborative Modeling
By Deployment Mode
• Cloud-based
• On-premise / Local Server-based
• Hybrid Deployment
By End User
• Pharmaceutical Companies
• Large Pharma
(e.g., Novartis, Pfizer)
• Mid-sized
Pharma
• Biotechnology Companies
• Contract Research Organizations (CROs)
• Academic & Research Institutes
• AI Drug Discovery Startups
By Therapeutic Area
• Oncology
• Neurological Disorders
• Infectious Diseases
• Cardiovascular Diseases
• Autoimmune & Inflammatory Diseases
• Rare & Orphan Diseases
• Others (Dermatology, Endocrinology)
By Region
• North America
• U.S.
• Canada
• Asia Pacific
• China
• Japan
• India
• South Korea
• Thailand
• Europe
• Germany
• UK
• France
• Italy
• Spain
• Sweden
• Denmark
• Norway
• Latin America
• Brazil
• Mexico
• Argentina
• Middle East and Africa (MEA)
• South Africa
• UAE
• Saudi Arabia
• Kuwait
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Healthcare:
The global drug
discovery as a service market is experiencing robust growth, reaching
an estimated USD 24.32 billion in 2025up from USD 21.3 billion in 2024. This
market is projected to climb to nearly USD 79.82 billion by 2034, expanding at
a healthy CAGR of 14.17% over the forecast period.
The computer-aided
drug design (CADD) market is gaining strong momentum worldwide, with
expectations of generating hundreds of millions in revenue between 2025 and
2034 as pharma companies increasingly adopt digital tools to streamline drug
discovery.
Similarly, the drug
discovery SaaS platforms market is witnessing rapid progress, driven by
the demand for cloud-based solutions that enhance collaboration, data
integration, and workflow automation throughout the drug development pipeline.
The generative
AI in drug discovery market is set to grow substantiallyfrom USD 250
million in 2024 to USD 318.55 million in 2025, and it’s expected to hit USD
2.85 billion by 2034. With a remarkable CAGR of 27.42%, this market is being
fueled by AI’s ability to accelerate molecule design and predict therapeutic
outcomes.
The AI
and ML in drug development market is also advancing quickly, supported
by growing investments in data-driven drug design and predictive analytics,
with revenue expected to surge significantly in the coming years.
Meanwhile, the robotics
in drug discovery market is on a strong growth trajectory from 2024 to
2034, thanks to innovations in automation, robotics, and high-throughput
screening that are revolutionizing lab workflows.
In the field of cancer research, the oncology
drug discovery market is expanding rapidlydriven by advances in
precision medicine, AI-powered drug design, and a growing portfolio of targeted
therapies aimed at improving patient outcomes.
The drug
designing tools market is growing steadily as well, reaching USD 3.7
billion in 2025 (up from USD 3.4 billion in 2024), and is expected to reach USD
7.86 billion by 2034. This growth is being propelled by increasing demand for in-silico
modeling tools and simulation software, at a CAGR of 8.73%.
The multiomics
in drug discovery market is showing strong promise, with projected revenue
in the hundreds of millions by 2034, as pharmaceutical companies leverage
genomics, transcriptomics, proteomics, and metabolomics to discover novel
therapeutics.
The connected
drug delivery devices market is expanding rapidlyfrom USD 7.44 billion
in 2024 to an expected USD 61.08 billion by 2034. With a CAGR of 23.44%, the
surge is driven by the rise in smart inhalers, auto-injectors, and digital adherence
tracking.
The needle-free
drug delivery devices market is gaining traction as well, growing from
USD 14.24 billion in 2024 to a projected USD 29.54 billion by 2034, at a CAGR
of 7.54%, fueled by patient preference for painless and convenient alternatives
to traditional injections.
In manufacturing, the computer
vision for drug production is emerging as a transformative tool, with
expectations of generating hundreds of millions in revenue globally between
2025 and 2034 by improving quality assurance and defect detection processes.
The drug
discovery platforms market is showing steady growthfrom USD 186.24 million
in 2024 to USD 635.45 million by 2034. With a CAGR of 13.44%, this segment
benefits from rising demand for integrated solutions that combine AI, big data,
and cloud computing.
The drug
screening market continues to expand, rising from USD 6.15 billion in
2023 to an estimated USD 10.34 billion by 2034. With a CAGR of 4.84%, this
growth is driven by the increasing need for early toxicity detection and
high-throughput screening.
The drug-device
combination products market is set to nearly doublegrowing from USD
150.3 billion in 2023 to USD 337.81 billion by 2034. This steady rise (CAGR of
7.64%) is fueled by the demand for advanced therapies that merge diagnostics
and therapeutics.
Lastly, the advanced
drug delivery market is expected to grow from USD 256.94 billion in
2025 to USD 385.14 billion by 2034. With a CAGR of 4.6%, the sector is evolving
through the development of more effective, targeted, and patient-friendly
delivery methods.
