AI in Drug Discovery Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028, Segmented By Component Type (Software and Services), By Drug Type (Small Molecule and Large Molecule), By Application Type (Preclinical Testing, Drug Optimization, and Repurposing, Target Identification, Candidate Screening, and Others), By Therapeutic Area (Oncology, Neurodegenerative Diseases

Market Overview

Global AI in Drug Discovery Market has valued at USD

Key Market Drivers

Decreased Absolute Time Spent on The Medication Research Process

The growing desire to reduce the overall time required for the drug discovery process would significantly boost the demand for artificial intelligence (AI) in pharmaceutical discovery, thereby accelerating market growth. Traditional animal models typically take three to five years to identify and optimize compounds before human evaluation, whereas AI-powered startups could potentially discover and develop novel drugs in a matter of days or months. Additionally, increased healthcare budget and advancements in healthcare infrastructure would serve as significant drivers for market expansion. The increased adoption of artificial intelligence (AI) to efficiently explore drug activity will fuel the demand for artificial intelligence (AI) in the drug development industry. Conventional drug discovery processes are characterized by their time-consuming nature, high costs, and susceptibility to failures. In contrast, AI-driven approaches present an opportunity to enhance efficiency and reduce expenses by streamlining critical stages of drug discovery, including compound screening, lead optimization, and clinical trial design. Leveraging AI algorithms enable rapid analysis of extensive compound libraries, efficient candidate prioritization, and accurate property predictions, thereby facilitating expedited and effective drug development.

Big Tech and Pharmaceutical Companies Investing Together

To facilitate the utilization of Microsoft's AI algorithms on the vast datasets employed in the pharmaceutical industry, Novartis and the computer company forged a strategic agreement lasting several years, commencing in 2019. The two entities expressed their intention to employ image analysis and generative methods to advance personalized medicine and enhance cell and gene therapy. In April, Nvidia, a prominent manufacturer of graphics processing units and a company actively advancing AI capabilities, partnered with Schrödinger to expedite and enhance the software's predictive capabilities in molecular forecasting. These factors collectively exert a substantial influence on the AI in Drug Discovery Market. Exscientia, among numerous enterprises established within the past decade centered around AI-based methodologies for drug discovery and development, has recently attracted substantial investment. Several of these companies are developing tools to accelerate the identification of potential small-molecule drug candidates. For example, Recursion Pharmaceuticals raised $436 million in its initial public offering, generating vast volumes of customized cellular behavior data with the aim of leveraging AI to uncover biological insights that can inform the development of novel medications. Furthermore, IT firms such as IBM, Microsoft, and Google are actively investing and engaging in financial collaborations with pharmaceutical companies to bolster the advancement of AI in the Drug Discovery Market.

Increase in Incidence of Chronic Diseases

The prevalence of chronic diseases, such as diabetes, chronic obstructive pulmonary disease (COPD), coronary artery disease, arthritis, asthma, hepatitis, and cancer, has witnessed a significant rise in major regions worldwide. This can be attributed to the growing geriatric population (projected to exceed 20% of the global population by 2050), evolving lifestyles, and dietary changes resulting from rapid urbanization. According to the International Diabetes Federation, in 2021, diabetes affected a staggering 537 million individuals globally. Furthermore, the number of new cancer cases per year is expected to reach 643 million by 2030. Lung cancer stands as the primary cause of cancer-related mortality in the Asia Pacific region, with China alone accounting for over 50% of all cases. Cervical cancer is largely influenced by lifestyle changes and socio-cultural factors. Notable countries affected by breast cancer in the Asia Pacific region include India, Thailand, and China.

Technological Advancement

Advancements in AI technologies, such as machine learning, deep learning, and natural language processing, have greatly enhanced the capabilities of AI in analyzing and interpreting complex biological data. These advancements facilitate the integration of diverse data sources, including genomics, proteomics, and clinical data, resulting in more comprehensive insights and expedited decision-making in drug discovery. The exponential growth of biological data, encompassing genomic sequences, protein structures, and drug-target interactions, presents a wealth of opportunities for AI-driven analysis and modeling. The availability of large-scale datasets empowers AI algorithms to discern patterns, forecast compound properties, and generate innovative hypotheses, thereby enabling informed and data-driven decision-making in drug discovery.

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Key Market Challenges

Data Quality and Availability

AI heavily relies on high-quality, diverse, and comprehensive data for training and model development. However, ensuring the availability of reliable and well-curated datasets can be challenging, especially in the field of drug discovery where data privacy, intellectual property, and regulatory considerations are significant factors. Obtaining access to large and representative datasets that encompass diverse patient populations and disease types may pose a hurdle for AI-driven drug discovery efforts. AI algorithms, particularly deep learning models, often function as "black boxes," making it difficult to interpret the reasoning behind their predictions or recommendations. In drug discovery, where transparent and explainable decision-making is crucial, the lack of interpretability may raise concerns among regulators, clinicians, and patients. Addressing the challenge of interpretability in AI models is vital to foster trust and acceptance in the field. Validating AI-driven models and ensuring compliance with regulatory standards present challenges in the drug discovery industry. Regulators typically require a high level of evidence and validation to ensure the safety and efficacy of new drugs. AI models must meet rigorous standards and demonstrate robust performance on diverse datasets to gain regulatory approval. Developing a regulatory framework that adequately addresses the unique considerations of AI in drug discovery is essential to facilitate its wider adoption.

Technical Challenges

Artificial intelligence and machine learning have made significant advancements in many aspects. However, the quality of data sets remains a substantial obstacle in utilizing AI methods for drug development. The existence of numerous challenging data sets underscores the importance of cooperation in advancing the use of AI in drug discovery. Addressing challenging issues related to data ownership and confidentiality is crucial. Despite the lack of strong initial leads in the field, there are ongoing efforts to retrospectively verify and optimize current technologies.

Key Market Trends

Expansion in R&D Venture

The expansion of research and development activities, coupled with the increasing utilization of cloud-based services and applications, presents favorable prospects for the growth of the artificial intelligence (AI) in drug discovery market. The growing demand from emerging countries and the advancement of biotechnology industries will further accelerate the pace of development in the AI in drug discovery market. The COVID-19 pandemic has significantly propelled the use of artificial intelligence in the field of drug development, as evidenced by its extensive application in the identification and screening of existing drugs for the treatment of COVID-19. AI has proven to be effective in identifying active substances for the prevention of various diseases such as SARS-CoV, HIV, SARS-CoV-2, influenza virus, and others. During the pandemic, economies worldwide relied on AI-based drug discovery instead of traditional vaccine discovery processes, which are time-consuming and costly, thus contributing to the growth of AI in the drug discovery market.

Personalized Medicine and Precision Healthcare

AI has the potential to revolutionize personalized medicine through the integration of patient data, including genetic information, clinical parameters, lifestyle factors. By analyzing this data, AI algorithms can identify patient subgroups, predict individual responses to therapies, and optimize treatment strategies. The ability to tailor treatments to individual patients based on their unique characteristics enables precision healthcare approaches that enhance treatment outcomes, minimize adverse effects, and optimize patient care. This transformative use case has the potential to revolutionize disease diagnosis, monitoring, and treatment, leading to more effective and personalized therapeutic interventions. The identification and validation of suitable drug targets are critical steps in the drug discovery process. Through the analysis of complex biological data, such as genomics, proteomics, and clinical data, AI algorithms can identify potential targets and elucidate their biological mechanisms. By integrating diverse data sources and leveraging machine learning techniques, AI can uncover novel drug targets, validate their relevance to specific diseases, and predict the likelihood of success in drug development. This use case empowers researchers to focus their efforts on targets with a higher probability of therapeutic success.

Segmental Insights

Component Type Insights

The AI in Drug Discovery Market is categorized into Software and Services based on component type. In terms of market share, the services segment is expected to dominate the global AI in drug discovery services market in 2022 and exhibit the highest CAGR between 2022 and 2028. The growth of this market segment is primarily driven by the advantages associated with AI services and the strong demand for AI services among end users. Moreover, the software segment also plays a significant role in the AI in Drug Discovery domain. For instance, several emerging companies are focusing on developing deep learning innovative solutions and generative models. These advancements enable the utilization of existing data to design molecules that can be optimized in silico, meeting all the success criteria of small molecule discovery projects. As an example, Makya is a user-friendly SaaS platform for AI-driven Novel Drug Discovery, with a specific focus on Multi Parametric Optimization for Ligand and structure-based projects.

Therapeutic Area Insights

In terms of therapeutic area, the oncology segment is anticipated to experience the highest compound annual growth rate (CAGR) during the forecast period. This can be attributed to the increased adoption of AI in discovering drugs for the treatment of various forms of cancer, a substantial number of promising drugs in the oncology pipeline, the growing use of AI in discovering and developing oncology drugs, and the rising number of collaborations between large pharmaceutical companies and AI providers. These factors are primarily driving the growth of this segment.

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Regional Insights

North America is poised to dominate the market, primarily due to the high adoption of AI technologies in pharmaceuticals, a substantial patient pool, a higher prevalence of chronic and infectious diseases, advanced healthcare infrastructure, and active clinical research and trials of AI and drug discovery in the region. The United States, in particular, exhibits a significant prevalence of metabolic and lifestyle diseases. As reported by the CDC, more than 130 million adults in the United States will be living with diabetes in 2022. Additionally, chronic kidney disease affects one in every seven adults in the country, as per the National Institutes of Health. Prominent research and academic institutions, such as the University of Texas MD Anderson Cancer Center, the University of Alabama in Huntsville, the University of Oxford, and the University of Dundee, among others, are integrating AI into drug discovery studies. The growth of artificial intelligence in the drug discovery market in the region is further propelled by key developments and a high concentration of market players in the United States. For example, in November 2021, Alphabet, the parent company of Google, unveiled ISOMORPHIC LABORATORIES as its inaugural drug discovery company. Similarly, in September 2022, Microsoft entered into a collaboration agreement with Novo Nordisk, offering its AI, computational, and cloud services for data science analysis, drug discovery, and development activities. Additionally, Johnson & Johnson's unit, Janssen, announced a partnership with SRI International in August 2022, leveraging SRI's SynFini AI platform for small molecule drug discovery. These ongoing advancements in the region are expected to drive market growth.

Recent Developments

• In February 2021, Exscientia and the University of Oxford will collaborate to investigate treatments for Alzheimer's disease.
• Commencing in October 2020, Beginning Therapeutics partnered with Genentech (US) for a joint effort in multi-target drug development, leveraging Genesis' advanced AI capabilities to identify potential therapeutics for various diseases.
• In March 2021, Iktos and Pfizer reached an agreement to utilize Iktos' AI-driven drug design tools for Pfizer's selected small molecule initiatives.

Key Market Players

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