How much is the deep learning in drug discovery and diagnostics market worth, and how is it expected to expand?
The deep learning in drug discovery and diagnostics market size has grown exponentially in recent years. It will grow from $8.21 $ billion in 2024 to $10.58 $ billion in 2025 at a compound annual growth rate (CAGR) of 28.8%. The growth in the historic period can be attributed to increased investment in AI research, growing demand for personalized medicine, increased funding from public and private sectors, rise in chronic diseases and complex conditions, and growing adoption of electronic health records.
The deep learning in drug discovery and diagnostics market size is expected to see exponential growth in the next few years. It will grow to $28.82 $ billion in 2029 at a compound annual growth rate (CAGR) of 28.5%. The growth in the forecast period can be attributed to improved diagnostic accuracy, growing collaboration between tech companies and pharmaceutical firms, progress in explainable AI (xai) for better model transparency, increased emphasis on real-world evidence (RWE), and expansion into emerging markets. Major trends in the forecast period include the adoption of AI technologies, advancements in computational power, integration of big data, development of novel deep learning architectures, and expansion of cloud-based platforms for AI.
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Which industry factors have accelerated the deep learning in drug discovery and diagnostics market’s expansion?
The growing demand for personalized medicine is expected to propel the growth of deep learning in drug discovery and diagnostics market going forward. Personalized medicine tailors medical treatment and interventions to individual genetic, environmental, and lifestyle factors to optimize efficacy and minimize side effects. The growing demand for personalized medicine is driven by advancements in genomics and data analytics, which enable more precise and effective treatments based on individual patient profiles. Deep learning in drug discovery and diagnostics enhances personalized medicine by analyzing complex biological data to identify unique biomarkers and predict patient responses to treatments more accurately. For instance, in February 2024, according to the Personalized Medicine Coalition, a US-based non-profit organization, the FDA approved 16 novel personalized therapies for patients with rare diseases in 2023, compared to six in 2022. Therefore, the growing demand for personalized medicine drives deep learning in drug discovery and diagnostics market.
What are the primary segments of the deep learning in drug discovery and diagnostics market?
The deep learning in drug discovery and diagnosticsmarket covered in this report is segmented –
1) By Type: Drug Discovery, Diagnostics, Forensic Interventions, Other Types
2) By Drug Type: Small Molecule Drugs, Biologics Drugs
3) By End Use Industry: Pharmaceutical Companies, Biotechnology Companies, Contract Research Organizations (CROs), Healthcare Information Technology (IT)
Subsegments:
1) By Drug Discovery: Target Identification, Drug Screening, Lead Optimization, Predictive Modeling
2) By Diagnostics: Medical Imaging Analysis, Genomic Data Interpretation, Disease Detection Algorithms, Personalized Medicine
3) By Forensic Interventions: Digital Pathology, Forensic DNA Analysis, Toxicology Screening, Crime Scene Reconstruction
4) By Other Types: Biomarker Discovery, Predictive Analytics in Healthcare, Patient Data Management Systems
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Which firms are leading the deep learning in drug discovery and diagnostics market?
Major companies operating in the deep learning in drug discovery and diagnostics market are Google Inc., Microsoft Corporation, International Business Machines Corporation, NVIDIA Corporation, Zebra Medical Vision Ltd., Tempus Labs Inc., Nanostring Technologies Inc., Owkin Inc., Insilico Medicine Inc., SOPHiA GENETICS SA, Qureai Technologies Pvt Ltd., H2Oai Inc., Arterys Inc., Deep Genomics Inc., GNS Healthcare Inc., MedAware Systems Inc., PathAI Inc., Kheiron Medical Technologies Ltd., CureMetrix Inc., OncoImmunity AS, Proscia Inc., BenevolentAI Ltd., BioXcel Therapeutics Inc.
Which market trends are set to define the future of the deep learning in drug discovery and diagnostics market?
Major companies operating in the deep learning in drug discovery and diagnostics market are focusing on the advancement of high-performance computing technologies, such as supercomputers, to accelerate innovation through improved performance and efficiency in complex tasks and AI applications. Advancements in supercomputers enhance deep learning in drug discovery and diagnostics by speeding up complex data analysis and simulations, leading to faster identification of drug candidates and more accurate diagnostics. For instance, in November 2022, Hewlett-Packard Enterprise, a US-based information technology company, introduced the HPE Cray EX and HPE Cray XD supercomputers. These new systems offer powerful performance and AI-at-scale capabilities in a more compact and cost-efficient design, aimed at accelerating drug discovery and speeding up disease treatments. This enables research scientists and pharmaceutical labs to gain deeper insights into chemical interactions, fostering the development of innovative drug therapies for both challenging and newly emerging diseases.
Which geographic trends are shaping the deep learning in drug discovery and diagnostics market, and which region has the highest market share?
North America was the largest region in the deep learning in drug discovery and diagnostics market in 2024. The regions covered in the deep learning in drug discovery and diagnostics market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Does The Deep Learning In Drug Discovery And Diagnostics Market Report 2025 Offer?
The deep learning in drug discovery and diagnostics market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
Deep learning in drug discovery and diagnostics refers to the use of advanced neural networks and artificial intelligence (AI) techniques to analyze complex biological and chemical data for predicting drug interactions, identifying disease patterns, and enhancing personalized medicine. Deep learning enhances drug discovery and diagnostics by improving prediction accuracy, automating data processing, and enabling personalized medicine through advanced analysis of complex biological and chemical data.
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