Global Generative AI in Drug Discovery Market By Technology (Machine Learning, Reinforcement Learning, and other), By End-User (Pharmaceutical and Biotechnology Companies, and Others), By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2023-2032

Report Overview

Generative AI in Drug Discovery Market size is expected to be worth around USD 1129 Mn by 2032 from USD 109 Mn in 2022, growing at a CAGR of 27.1% during the forecast period from 2023 to 2032.

Request Covid - 19 Impact

The generative AI in drug discovery market refers to the market for the uses of generative artificial intelligence technologies in the drug discovery process. Generative AI is a type of machine learning that involves the formation of new data like new molecules, based on patterns learned from existing data. In the context of drug discovery, generative AI algorithms can be trained on large databases of known drug molecules and their properties to create new compounds that are predicted to have therapeutic effects. The generative AI in drug discovery market is driven by the maximizing demand for the most efficient & effective price drug discovery processes. Traditional drug discovery methods are consuming time & expensive and often involve the screening of big numbers of compounds that may not be effective. By creating new drug candidates that are optimized for particular qualities and are more likely to be successful in treating a specific condition, generative AI offers the potential to speed up the drug discovery process.

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The market for generative AI in drug discovery is expected to increase rapidly in the becoming years, driven by the increasing adoption of AI technologies in the pharmaceutical industry and the rising availability of large datasets of chemical compounds. The market is highly competitive, with a few key players developing and offering generative AI platforms for drug discovery.

Driving Factors

Maximizing Demand for the Most Efficient and Effective Price Drug Discovery Processes

The generative AI in drug discovery market is driven by several factors. One of the important drivers is the maximizing demand for the most efficient and effective price drug discovery processes. The old drug discovery process is consuming time & expensive and often involves the screening of the biggest numbers of compounds that may not be effective. Generative AI has the potential to accelerate the drug discovery process by generating new drug candidates that are optimized for specific properties and are most likely to be effective in treating a particular disease. Another driving factor is the rising availability of bigger datasets of chemical compounds. The pharmaceutical industry is generating wide amounts of data on drug molecules and AI algorithms can be used to analyze this data as well as define new drug candidates. Additionally, advancements in computing power and cloud-based computing have made it possible to process and analyze large datasets of chemical compounds in real-time, further driving the rise of the generative AI in drug discovery market. The maximizing adoption of AI technologies in the pharmaceutical industry is another driving factor. Many pharmaceutical companies are spending heavily in AI technologies to optimize drug discovery processes and minimize the time & cost of bringing new therapies to market. Generative AI is a key area of focus for most of these companies, and there is significant potential for rise in the market as a result.

Restraining Factors

Lack of Skilled Professionals and High Costs

Despite the potential advantages of using generative AI in drug discovery, there are a few restraining factors that may impact the rise of this market. Firstly, the shortage of transparency in AI algorithms can be a hindrance in gaining regulatory approval. Regulators may not fully understand how the AI systems are creating drug candidates, which can lead to uncertainty & potential safety concerns. Additionally, the high price of implementing AI technology in drug discovery can limit adoption, particularly for smaller biotech & pharmaceutical companies. The need for big amounts of data to train AI models is also a challenge, as it can be difficult to obtain & maintain the highest quality datasets. Furthermore, the shortage of standardized evaluation metrics for generative AI models can make it difficult to compare the performance of different systems. These factors may limit the widespread adoption of generative AI in drug discovery, but with continued research & development, these challenges may be overcome.

COVID-19 Impact Analysis

The COVID-19 has had a significant impact on the pharmaceutical industry, including the uses of generative AI in drug discovery. On one hand, the COVID-19 has highlighted the need for rapid drug development and discovery, which has led to maximized interest in the use of AI in this field. The ability of generative AI to quickly generate potential drug candidates and predict their efficacy has been particularly valuable in the fight against COVID-19.

On the other hand, the Covid-19 has also caused disruptions in the supply chain and led to delays in clinical trials, which has affected the overall drug development process. This has created challenges for the implementation of generative AI in drug discovery, as access to big & diverse datasets has been limited. Additionally, the COVID-19 has also resulted in financial constraints for many companies, which may limit investment in AI technology. Furthermore, the regulatory landscape has also been impacted by the pandemic. Regulators have had to adapt quickly to the changing situation and have been the most flexible in their approach to drug approvals, which has made uncertainties around the use of AI in drug discovery.

By Technology Analysis

The Deep Learning Segment Holds a Significant Share in the Technology Segment in the Generative AI in Ecommerce Market.

Based on technology, the market is divided into machine learning, reinforcement learning, deep learning, molecular docking, and quantum computing. Among these the deep learning segment is dominant in the technology segment in generative AI in ecommerce market, with a market share of 36%. Owing to deep learning is a subset of machine learning that focus on artificial neural networks with multiple layers, enabling the model to learn & extract complex patterns and features from data. It is inspired by the structure & function of the human brain and its interconnected neural networks. Deep learning has gained significant attention & popularity owing to its ability to handle large and high dimensional datasets and its capacity to automatically learn hierarchical representations of data. By utilizing multiple layers of interconnected neurons, deep learning models can learn abstract and hierarchical representations of data, capturing intricate patterns and relationships.

By End-User Analysis

Pharmaceutical and Biotechnology Holds a Significant Share in the End-User Segment of Generative AI in Drug Discovery Market.

Based on end-user, the market is divided into pharmaceutical and biotechnology companies, academic and research institutions, contract research organizations and other end-users. Among these the pharmaceutical and biotechnology segment dominates the market with a revenue share of 42% in the forecasted period. Pharmaceutical and biotechnology companies are the primary end-users of generative AI in drug discovery. They leverage generative AI technologies to accelerate the drug discovery process, identify new drug candidates, optimize lead compounds, and improve target selection. Pharmaceutical and biotechnology companies invest in generative AI platforms and tools to enhance their research and development capabilities and bring innovative drugs to market more efficiently.

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Generative AI in Drug Discovery Market Segments

Based on Technology

• Machine learning
• Reinforcement learning
• Deep learning
• Molecular docking
• Quantum computing

Based on End-User

• pharmaceutical and biotechnology companies
• academic and research institutions
• contract research organizations (CROs)
• Other End-Users

Growth Opportunity

Minimize the Time and Cost of Drug Development

The use of generative AI in drug discovery presents significant rise opportunities for the pharmaceutical industry. The ability of AI to repeatedly generate and screen potential drug candidates can significantly minimize the time & cost of drug development. It can also lead to new treatment options for many diseases. Some of these diseases are hard to treat or have no effective treatment. The use of AI generative helps identifies drug targets previously overlooked. This leads to the development of new therapeutic paths and drug classes. It can help improve patient outcomes and increase the number of treatment options available. AI can be used to discover new drugs and also for personalized medicine. This is when the drug formulations are customized based on a patient's genetic make-up & other factors. This can lead to the most effective treatments and fewer adverse effects. Moreover, the rise of the generative AI in drug discovery market is also being driven by advancements in AI technology like the development of the most advanced algorithms and the availability of larger & the most diverse datasets. This enables the most accurate predictions and better screening of potential drug candidates.

Latest Trends

Maximized Uses of Deep Learning Algorithms

The use of generative AI in drug discovery is a repeatedly evolving field, and there are a few latest trends that are shaping its development. One trend is the maximized uses of deep learning algorithms, which can analyze larger & the most complex datasets than traditional machine learning algorithms. This has led to enhanced accuracy in predicting potential drug candidates & drug targets. Another trend is the uses of generative adversarial networks, which can generate novel compounds that are structurally different from known drugs. This can help to identify new therapeutic pathways and drug classes, leading to the development of more effective treatments. The integration of AI with other technologies like high-throughput screening & lab automation is also a rising trend. This can further accelerate the drug discovery process and minimize the time and price of developing the latest treatments. Finally, the use of explainable AI is becoming increasingly important in drug discovery. Explainable AI can provide insights into how AI models are making predictions, which can increase transparency and help to address regulatory concerns around the use of AI in drug development.

Regional Analysis

North America Accounted the Largest Revenue Share in Generative AI in Drug Discovery Market in 2022.

North America is the leading region in the global Generative AI in Drug Discovery Market. Owing to the presence of leading pharmaceutical companies, well established research infrastructure and a supportive regulatory environment. The United States is the highest market in the region, with significant spend in AI technology for drug discovery & development. Europe is also expected to experience significant growth in the generative AI in the drug discovery market, with a strong emphasis on research & development in the pharmaceutical industry and the availability of government funding for innovative technologies.

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Key Regions

• North America
  • The US
  • Canada
  • Mexico
• Western Europe
  • Germany
  • France
  • The UK
  • Spain
  • Italy
  • Portugal
  • Ireland
  • Austria
  • Switzerland
  • Benelux
  • Nordic
  • Rest of Western Europe
• Eastern Europe
  • Russia
  • Poland
  • The Czech Republic
  • Greece
  • Rest of Eastern Europe
• APAC
  • China
  • Japan
  • South Korea
  • India
  • Australia & New Zealand
  • Indonesia
  • Malaysia
  • Philippines
  • Singapore
  • Thailand
  • Vietnam
  • Rest of APAC
• Latin America
  • Brazil
  • Colombia
  • Chile
  • Argentina
  • Costa Rica
  • Rest of Latin America
• Middle East & Africa
  • Algeria
  • Egypt
  • Israel
  • Kuwait
  • Nigeria
  • Saudi Arabia
  • South Africa
  • Turkey
  • United Arab Emirates
  • Rest of MEA

Market Share & Key Players Analysis

The market for generative AI in drug discovery is highly competitive, with a few key players vying for market share. These companies are spending heavily on research & development to develop advanced AI algorithms and expand their product offerings. For example, one of the key players in the market is Insilico Medicine that has developed a deep learning platform for drug discovery that uses AI to generate and screen potential drug candidates. The company has collaborations with leading pharmaceutical companies and has received significant investment from venture capital firms.

Market Key Players:

• Insilico Medicine
• Atomwise Inc.
• BenevolentAI
• XtalPi Inc
• Numerate Inc
• Cyclica Inc
• BioSymetrics
• Other Key Players

Recent Developments

• April 2021, Insilico Medicine announced that it had generated novel small molecules for a challenging protein target in a matter of weeks using its deep generative models. The company used its AI platform to identify potential drug candidates and then validated them using experimental assays.
• February 2021, Atomwise announced that it had raised $123 million in a Series B funding round, which it would use to expand its AI platform for drug discovery. The company's platform has been used to identify potential drug candidates for a variety of diseases, including COVID-19.

 Report Scope