(MENAFN- GlobeNewsWire - Nasdaq) AI-driven screening, molecular docking, genomic data and cloud platforms are accelerating drug discovery, creating opportunities in oncology, personalized medicine and predictive modeling.Dublin, Sept. 15, 2026 (GLOBE NEWSWIRE) -- "Computer-Aided Drug Discovery Market Report 2026" has been added to ResearchAndMarkets.com's offering.
Computer-Aided Drug Discovery Market to Reach $13.69 Billion by 2030 as AI Adoption Accelerates
The global computer-aided drug discovery market is projected to expand rapidly as pharmaceutical and biotechnology organizations increase their use of artificial intelligence, machine learning and advanced computational technologies. The market is expected to grow from $7.56 billion in 2025 to $8.54 billion in 2026, representing a compound annual growth rate (CAGR) of 12.9%.
Historical market growth has been supported by the increasing complexity of drug development, rising pharmaceutical research and development expenditure, expanding computational biology capabilities and greater availability of biological data. The adoption of in silico modeling techniques has also enabled researchers to evaluate potential drug candidates more efficiently and improve decision-making throughout the discovery process.
Looking ahead, the computer-aided drug discovery market is forecast to reach $13.69 billion by 2030, growing at a CAGR of 12.5%. Key growth drivers include increasing investment in AI-enabled drug discovery, demand for shorter development cycles, expansion of personalized medicine research and the growing integration of machine learning into pharmacology. Cloud computing is also supporting large-scale molecular simulations and enabling research teams to access advanced computational resources without investing extensively in on-site infrastructure.
Major computer-aided drug discovery market trends include the rising use of AI-driven virtual screening, molecular docking simulations and predictive modeling. Researchers are increasingly integrating genomic data into drug design to identify promising therapeutic targets and develop more precise treatments. The expansion of cloud-based drug discovery platforms is further improving collaboration, scalability and access to sophisticated research tools.
The rising prevalence of cancer is expected to remain an important driver of market growth. Lifestyle changes, urbanization, environmental pollution, tobacco and alcohol consumption, and differences in healthcare access and awareness are contributing to increasing cancer incidence. Computer-aided drug discovery supports the development of treatments that target cancer cells with specific genetic mutations, strengthening opportunities for personalized and potentially more effective therapies.
In July 2024, the Australian Institute of Health and Welfare reported that approximately 165,000 new cancer cases were diagnosed in Australia during 2023. Annual diagnoses are projected to reach approximately 204,000 by 2033, with an estimated 1.9 million cases expected between 2024 and 2033. This growing disease burden is increasing demand for technologies that can accelerate oncology drug research and improve candidate selection.
Innovation remains central to the competitive development of the computer-aided drug discovery market. In December 2023, Merck KGaA launched AIDDISONT, a software-as-a-service platform combining generative AI, machine learning, computer-aided drug design and synthesis planning. The platform can virtually screen more than 60 billion chemical targets and recommend suitable synthesis routes. Trained using two decades of pharmaceutical research and development data, it is designed to identify candidates with desirable characteristics, including stability and non-toxicity. Integration with Merck's SynthiaT retrosynthesis application programming interface also supports more efficient and sustainable manufacturing decisions, helping reduce drug development costs and timelines.
Strategic acquisitions are further reshaping the market. In October 2024, Certara Inc. acquired Hungary-based Chemaxon Ltd. for $90 million. The transaction allows Certara to integrate Chemaxon's cheminformatics and scientific informatics capabilities into its drug discovery portfolio. These capabilities include chemical structure drawing, property prediction, chemical database management and compound registration. The acquisition strengthens Certara's biosimulation and model-informed drug development services while broadening access to established tools such as Marvin, JChem and Chemicalize.
North America was the largest region in the computer-aided drug discovery market in 2025. The global market analysis covers Asia-Pacific, Southeast Asia, Western Europe, Eastern Europe, North America, South America, the Middle East and Africa. Countries covered include Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, the United Kingdom, the United States, Canada, Italy and Spain.
Markets Covered:
1) By Type: Structure Based Drug Design; Ligand-Based Drug Design; Sequence-Based Approaches
2) By Therapeutic Area: Oncology; Neurology; Cardiovascular Disease; Respiratory Disease; Diabetes; Other Therapeutic Areas
3) By End User: Pharmaceutical Companies; Biotechnology Companies; Research Laboratories
Subsegments:
1) By Structure Based Drug Design: X-ray Crystallography; Nuclear Magnetic Resonance (NMR) Spectroscopy; Molecular Docking Studies; Virtual Screening
2) By Ligand-Based Drug Design: Quantitative Structure-Activity Relationship (QSAR) Models; Pharmacophore Modeling; Ligand Similarity Searching; Fragment-Based Drug Design
3) By Sequence-Based Approaches: Protein Structure Prediction; Homology Modeling; Bioinformatics for Drug Target Identification; Genomic Data Analysis for Drug Discovery
Time Series: Five years historic and ten years forecast.
Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita.
Data Segmentation: Country and regional historic and forecast data, market share of competitors, market segments.
Key Attributes:
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