Generative AI In Pharmaceutical Market Size, Share, and Trends 2024 to 2034

Generative AI in Pharmaceutical Market Size and Forecast 2024 to 2034

The global generative AI in pharmaceutical market is surging, with an overall revenue growth expectation of hundreds of millions of dollars during the forecast period from 2024 to 2034.

Generative AI in Pharmaceutical Market Key Takeaways

• North America holds the largest share of the market, the region is expected to sustain the growth during the forecast period.
• By technology, the deep learning segment is expected to dominate the market over the forecast period.
• By drug type, the small molecule segment is expected to hold the largest market share over the forecast period.
• By application, the drug discovery segment is expected to dominate the market growth during the forecast period.

Market Overview

Generative AI is a subset of AI that employs formulas to generate data that is comparable to training data. The original data might be anything, including images, written text, or even molecular structure. Generative AI employs what is known as neural networks as a component of machine learning to recognize patterns in the original data and generate new data that resembles it. When it comes to finding and creating novel medications, this technology offers enormous potential in the pharmaceutical sector. Generative AI can speed up the drug development process, assisting pharmaceutical companies in developing novel medications more quickly and efficiently by producing new molecules with certain properties.

The numerous kind of generative AI tools used in the pharmaceutical industry includes generative adversarial networks, recurrent neural networks, variational autoencoders, deep reinforcement learning, and transformer models. GANs are utilized in pharmacy research to produce compounds and develop novel drugs. The generator network in GANs creates synthetic samples, while the discriminator network determines whether or not these samples are real. New molecules with the appropriate properties are produced as a result of this procedure.

Recurrent Neural Networks are utilized to generate sequential data that may be used to develop novel chemical structures or enhance the characteristics of medications. RNNs can create new sequences with the appropriate properties by learning the patterns in sequential data.

Variational Autoencoders aids in the development of new drugs. These tools are capable of producing new molecules with specified properties and are aware of the dispersion of chemical structures. They are incredibly helpful in exploring the chemical universe and developing a variety of substances.

According to the National Institutes of Health, Genomic data production is rapidly nearing 40 billion gigabytes annually. Findings that will expand knowledge of human health and promote precision medicine depend on the ability to exchange, analyze, and interpret genetic data.

Generative AI in Pharmaceutical Market Growth Factors

• Pharmaceutical sector prioritizing use of AI virtual chatbots to improve their consumer engagements.
• Increased demand for personalized medicines is holding potential for generative AI in pharmaceutical industry. 
• Generative AI is seeking growth in pharmaceutical for the requirement of clinical trial optimization, and prediction capability to reduce cost and risks of clinical trials.
• Utilization of AI algorithms has increased in pharmaceutical sector for effective identification of potential drug targets.
• AI is able to learn from previous data and libraries about certation of new chemical structures with improved drug-related qualities, which can help pharmaceutical manufacturer industry to reduce risk and product toxicity.

Market Scope

Market Dynamics

Driver

Adoption of AI for precise decision making

Pharmaceutical companies are adapting generative AI for better decision making. Pharmaceutical industries often face challenges for several designs of management of portfolio to pricking of products. AI enable to make easy and quick decisions by overcoming the boundaries. AI can analysis patient data, clinical outcomes, and market trend information. It helps companies to make intelligent choice and risk reductions, and invests money more systematically by including AI technologies in their decision-making process. Generative AI are capable to increase business growth and enhance patient care. Generative AI powered technology is driving its demand among medical experts and clients for better advice.

Restraint

Ethical concerns 

Market growth of generative AI can be restraint due to ethical concerns. Deep learning models uses in generative AI uses limited clarity and accessibility. Generative AI are made up of numerous layers, which provides different judgments according to the data they have already learn. Pharmaceutical industries can face difficulties while explaining models’ decisions. Additionally, several models can cat harmful and can be dangerous to customers.AI training with biased data collection likely to led them to false judgements. Generative AI are required to be trained and execute with cloud data management and data filters.

Technology insights

The Natural Language Processing (NLP)segment is projected as the fastest growing technology segment due to increased use of generative AI in pharmaceutical data. Date provided by drug discovery needed to be organized with support of natural language processing. It also helpful to detect the patient reactions on pharmaceuticals, allowing pharmaceutical companies to handle drug-related issues more effortlessly and efficiently. With the aim of enhancing data quality and shorter the time to market for new drugs, and to simplify research methods, natural language is necessary for understanding of complex language and gain relevant information. Need for improvement in productivity and innovations are driving the use of natural language processing, that boosting the use of generative AI in pharmaceuticals.

Application insights

The clinical trial research segment is accounted as the fastest growing segment in the market. generative AI utilizes in clinical trails research to improve patient selection and trail design, that is essential to boost the effectiveness and outcomes of clinical rails. AI can helps obtaining useful knowledge to design effective clinical trial research by recruiting and analysing numerous data from previous clinical studies. AI helps to promote suitable candidate for clinical trail process by studies their historical records, disease and health state. Additionally, clinical trial scientists have increased adaption of AI to keep eye on candidate while providing treatments. Ongoing innovations and need of clinical trails research holding great market potential for AI in pharma.

Regional Insights

North America is expected to dominate the market during the forecast period. The regional growth is driven by several factors including increasing AI usage in pharmaceutical clinical trials, rising drug discovery and development, rising prevalence of chronic diseases, and technical breakthroughs in the pharmaceutical sector. Moreover, the presence of major market players and growing business activities such as product launch, partnerships and collaboration are the key factors that propel the market growth in the region.

• In January 2023, Absci Corporation (Nasdaq: ABSI), which is a generative AI drug creation company, introduced its first creation and vailidation de novo antibodies in silico by using zero-shot generative AI, which was the key turning point for the biotechnology industry.
• For instance, in March 2022, Insilico Medicine and EQRx formed a strategic partnership to combine their respective capabilities in de novo small molecule creation, clinical development, and commercialization.

Collaboration of Singapore Health Services with the National University of Singapore (NUS) encouraged the innovation of SELENA+, which is an excellent AI eye imaging tool, utilizes for detection of early diagnosis of diabetic retinopathy, age-related macular degeneration, and glaucoma.

China detected as the leading country country in developments and adaption of AI tools for health. Chinese government is continuously focusing on the novel innovations in clinical trials and drug discovery. For instant, In August 2024, Children’s Hospital of Soochow University located in Jinji Lake, China has developed an LLM-based clinical decision support system for Gaucher disease, which is a rare illness poses by a high risk of misdiagnosis and delayed triage. This system helps experts in diagnosis treatment and monitors patients individually for the high-risk indicators according to the huge amount of data contained in electronic medical records.

• In august 2023, Pharos iBio, a South Korea based company, introduced a first AI-driven anticancer medication which addresses the FLT3 gene mutation. This innovation detected the wide rage application of AI methods in anti-cancer drug designs. 

Generative AI in Pharmaceutical Market Companies

• Bayer AG
• Insilico Medicine Inc.
• Atomwise Inc.
• BenevolentAI Ltd.
• Numerate Inc.
• XtalPi Inc.
• Berg Health LLC
• Conduent Incorporated
• Fujitsu
• OKRA.ai

Recent Developments

• In June 2023, Eli Lilly & Company and XtalPi Inc. launched an AI drug development partnership worth up to $250 million in upfront and milestone payments. The partnership will make use of XtalPi's robotics platform and integrated AI capabilities to create and deliver medication candidates from scratch for an unnamed target. In a partnership with the Lilly team, XtalPi will offer a novel chemical using their effective one-stop AI drug discovery solution, which Lilly will then pursue clinical and commercial development.
• In June 2023, the functioning of a conversation tool utilizing generative AI was disclosed by Sumitomo Pharma Co., Ltd. The Tool is an interactive online tool that performs duties similar to "ChatGPT" offered by OpenAI Inc. Although OpenAI's AI engine is utilized, the parameters prevent OpenAI from using the data in any other way. All workers of the company are expected to utilize the tool.
• In January 2023, Conduent Incorporated, a worldwide technology-led business process solutions provider, introduced the IntelliHealth module for healthcare provider interaction. This new module is an end-to-end solution to assist pharmaceutical businesses in improving access to and engagement with, healthcare professionals to generate targeted pharmaceutical sales and marketing. With powerful analytics, multichannel communication, and seasoned sales professionals and medical science liaisons, IntelliHealth allows pharma enterprises to power quicker and more effective product commercialization. The platform's robustness enables pharma businesses to decrease risk, control expenses, and gain competitive advantages while delivering ROI over the entire life cycle of medicinal treatment.

Segments Covered in the Report:

By Technology

• Deep Learning
• Natural Language Processing
• Querying Method
• Context-aware Processing
• Others

By Drug Type

• Small Molecule
• Large Molecule

By Application

• Clinical Trial Research
• Drug Discovery
• Research And Development
• Others

By Geography

• North America
• Europe
• Asia-Pacific
• Latin America
• Middle East and Africa