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 discovery and development |
|
Recursion Pharmaceuticals (February 2025) |
Announced significant clinical data on lead AI-based drug candidate for brain disease |
|
Recursion Pharmaceuticals (October 2024) |
Collaborated with Google Cloud to support Recursion's drug discovery platform |
|
Insilico Medicine (October 2024) |
Acquired a major step in AI-powered drug discovery by collaborating with Sanofi |
|
Exscientia (August 2024) |
Agreed with Recursion to develop a global technology-enabled drug discovery leader with end-to-end capabilities |
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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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.
