Artificial Intelligence (AI) in Drug Discovery Market Industry Developments, Trends, Forecast 2030

The artificial intelligence (AI) in drug discovery market centers on using advanced algorithms, machine learning, and deep learning to accelerate the identification and development of new drugs. AI platforms analyze massive biological, chemical, and clinical datasets to predict drug-target interactions, optimize lead compounds, and streamline preclinical and clinical trial design, reducing time and cost compared to traditional methods. Market growth is driven by the rising need for faster, more cost-efficient drug development, the increasing complexity of diseases such as cancer and neurological disorders, and expanding collaborations between pharmaceutical companies and AI technology providers. Key applications include target identification, molecule screening, biomarker discovery, and drug repurposing. North America leads the market due to strong R&D investment, robust biotech ecosystems, and supportive regulatory frameworks, while Europe follows closely. Asia-Pacific is emerging rapidly as governments and pharmaceutical companies invest in AI-driven research. Challenges include data privacy concerns, integration with existing drug development pipelines, and the need for high-quality training data.

The global artificial intelligence (AI) in drug discovery market size was valued at USD 3.00 billion in 2022 and is projected to grow from USD 3.54 billion in 2023 to USD 7.94 billion by 2030, exhibiting a CAGR of 12.2% during the forecast period (2023–2030). North America dominated the market with a 69.33% share in 2022, supported by strong R&D investments, early adoption of AI-driven platforms, and the presence of leading pharmaceutical and biotech companies.

Top Companies in the Market

• Microsoft (U.S.)

• Schrödinger, Inc. (U.S.)

• Cresset (U.K.)

• IBM (U.S.)

• Atomwise Inc. (U.S.)

• Insilico Medicine (U.S.)

• Exscientia (U.K.)

• BenevolentAI (U.K.)

• Aria Pharmaceuticals, Inc. (U.S.)

• Integral BioSciences (U.S.)

• Alphabet Inc. (U.S.)

Key Industry Development

In November 2022, Cyclica received a USD 1.8 million grant from the Bill & Melinda Gates Foundation. The grant is to apply its AI-enabled drug discovery platform to discover new non-hormonal contraceptives, leveraging multiple low-data biological targets.

Market Drivers

• Increasing prevalence of chronic diseases: Rising cases of cancer, cardiovascular, and other chronic diseases are driving demand for novel therapies, supported by AI tools.

• High costs and lengthy timelines in traditional drug discovery: New drug development often takes ~14.6 years and costs around USD 2.6 billion. AI reduces time and cost by streamlining processes.

• Strategic collaborations & partnerships: Pharmaceutical firms are increasingly collaborating with AI providers to accelerate discovery.

• Advancement in computational technologies: Machine learning and NLP support predictive modelling, molecular design, and virtual screening.

• Cloud adoption & data availability: Cloud platforms and access to genomic, clinical, and biological data enhance AI efficiency.

Market Restraints

• Lack of standardization in data quality: Data inconsistencies and small sample sizes challenge AI algorithm performance.

• Complexity in large molecule drug discovery: Large molecules require extensive resources and are harder to analyze, limiting AI applications.

• Skill gaps & service costs: Limited skilled workforce and high costs restrict broader adoption.

Segmentation Analysis

By Drug Type

• Small Molecule: Dominated the market in 2022 due to availability of extensive data, predictable properties, and cost-effectiveness.

• Large Molecule: Smaller share but growing with investments in biologics research.

By Offering

• Software: Held the largest share in 2022, supported by innovative platforms with advanced features.

• Services: Smaller share due to higher costs and lack of skilled expertise.

By Technology

• Machine Learning (ML): Leading segment, applied in predictive modelling, drug repurposing, and clinical trials.

• Natural Language Processing (NLP): Extracts insights from unstructured data like medical records and scientific articles.

• Others: Includes computer vision and robotics, still in early adoption.

By Application

• Oncology: Largest application area, driven by high cancer prevalence and need for targeted therapies.

• Neurology: Significant share with growing R&D in neurodegenerative disorders.

• Cardiology & Endocrinology: Smaller segments with fewer drug candidates currently in pipeline.

• Others: Includes additional therapeutic areas under exploration.

By End-user

• Pharmaceutical & Biotechnological Companies: Leading end-user segment due to extensive integration of AI tools.

• Academic & Research Institutes: Second largest segment, driven by government funding and pilot studies.

• Others: Includes contract manufacturers and healthcare facilities with slower adoption rates.

Regional Insights

• North America: Valued at USD 2.08 billion in 2022. Leading region with strong pharmaceutical presence and high R&D investments.

• Europe: Second-largest region, emphasizing cost reduction and treatment innovation through AI.

• Asia Pacific: Expected to register the fastest growth due to rising chronic disease prevalence and adoption of advanced technologies.

• Latin America & Middle East & Africa: Smaller markets with slower adoption due to limited infrastructure and funding.

Explore the full research report with detailed insights and TOC:https://www.fortunebusinessinsights.com/artificial-intelligence-in-drug-discovery-market-105354 

Future Market Scope

The market is expected to expand significantly as AI technologies mature. Growth drivers include increasing investments in biologics, collaborations between AI providers and pharmaceutical companies, and wider adoption across emerging regions. Efforts to standardize data and regulatory support will further accelerate adoption.

Conclusion

The Artificial Intelligence in Drug Discovery Market is on a strong growth trajectory. With advancements in AI models, rising collaborations, and demand for efficient drug development, the market is projected to more than double by 2030. Despite challenges such as data quality and skill shortages, continuous innovation will ensure sustained growth and transformative impact on the pharmaceutical industry.

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