Report Description

Forecast Period


Market Size (2022)

USD 750.04 Million

CAGR (2023-2028)


Fastest Growing Segment


Largest Market

North America

Market Overview

Global AI in Drug Discovery Market has valued at USD 750.04 Million in 2022 and is anticipated to project robust growth in the forecast period with a CAGR of 10.18% through 2028. Artificial intelligence is a field in computer science that focuses on simulating intelligent behavior. It grants computers the ability to think and perform various tasks, similar to humans and animals, while learning from errors in the process. Artificial intelligence typically employs algorithms designed to enable efficient task performance with minimal errors. By leveraging deep learning and machine learning algorithms, artificial intelligence applies personalized knowledge to accomplish diverse tasks. The application of artificial intelligence in the field of drug discovery is highly significant. It aids in disease tracking, facilitates the development of disease treatments, and even predicts the emergence of mutated animal viruses. Artificial intelligence has greatly enhanced research and development in drug discovery, leading to the discovery of treatments for chronic diseases. The drug discovery and development processes in medicine, pharmacology, and biotechnology involve identifying biological targets such as enzyme proteins, genes, or receptors, with the aim of creating and acquiring drugs. Previously, drugs were discovered by identifying active ingredients in conventional treatments. Hence, drug discovery serves as the initial step in identifying potential novel drugs and their medicinal purpose. The evaluation of new drugs now includes factors such as bioavailability, efficacy, potency, and toxicity.

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.