Generative AI in Drug Discovery Market Size [+USD 1,129 Mn] | Expands Steadily at a CAGR of 27.1% by 2032

New York, June 13, 2023 (GLOBE NEWSWIRE) — According to MarketResearch.Biz, the generative AI in drug discovery market size is projected to surpass around USD 1,129 million by 2032, and it is poised to reach a CAGR of 27.1% from 2023 to 2032.

The global generative AI in drug discovery market accounted for USD 109 million in 2022. 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.

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Key Takeaway:

• By technology, the deep learning segment has dominated the market, and it accounted for the most prominent global revenue of 36% in 2022.
• By end-user, the pharmaceutical and biotechnology segment generated the largest revenue share of 42% in 2022.
• In 2022, North America dominated the market with the highest revenue share of 49%.
• The Asia Pacific region is expected to grow at a significant CAGR from 2023 to 2032.

Factors Affecting the Growth of the Generative AI in Drug Discovery Market

• Increasing Complexity of Drug Discovery: The drug discovery process is coming more complex, requiring advanced tools and techniques to define potential drug candidates. Generative AI supplies a solution by leveraging its ability to operate large datasets, extract complex patterns as well as generate novel molecules with desired properties.
• Advancements in Artificial Intelligence: Continuous advancements in artificial intelligence, including deep learning and machine learning techniques, enhance the capabilities of generative AI in drug discovery. These advancements enable more accurate predictions, efficient optimization, and improved generation of drug candidates.
• Availability of Big Data and Molecular Information: The increasing availability of large-scale datasets and molecular information, such as chemical structures, biological assays, and genomic data, provides a rich resource for training and validating generative AI models. This abundance of data enhances the performance and accuracy of generative AI in drug discovery.
• Collaborations and Partnerships: Collaborations between pharmaceutical companies, AI technology providers, academic institutions, and research organizations drive innovation in generative AI for drug discovery. By combining expertise, resources, and data, these collaborations accelerate the development and adoption of generative AI in the pharmaceutical industry.
• Regulatory Support and Recognition: Regulatory agencies are recognizing the potential of AI-driven technologies in drug discovery and providing support and guidance for their integration into the drug development process. This regulatory support boosts confidence in the use of generative AI and facilitates its implementation.
• Addressing Unmet Medical Needs: Generative AI in drug discovery has the potential to address unmet medical needs by enabling the discovery of new drug candidates for diseases that currently lack effective treatments. This potential to make a significant impact on patient outcomes drives interest and investment in generative AI.

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Top Trends in Global Generative AI in Drug Discovery Market

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 use 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.

Market Growth

The increasing availability of large-scale datasets, molecular information, and biomedical databases provides a wealth of data for training and validating generative AI models. Integrating diverse data sources with generative AI techniques allows for more comprehensive and accurate drug discovery efforts. Collaborations between pharmaceutical companies, AI technology providers, academic institutions, and research organizations foster innovation in generative AI for drug discovery. These collaborations bring together expertise, resources, and data, driving the development and adoption of generative AI in the pharmaceutical industry. Regulatory agencies are recognizing the potential of AI-driven technologies in drug discovery and are supportive of their application. They provide guidance and frameworks for integrating generative AI into the drug development process, further boosting its adoption and market growth.

Regional Analysis

North America is the leading region in the global Generative AI in Drug Discovery Market. Owing to the presence of leading pharmaceutical companies, a well-established research infrastructure, and a supportive regulatory environment. The United States is the highest market in the region, with significant spending on 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.

Competitive Landscape

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 which 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.

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Scope of Report

Market Drivers

The generative AI in drug discovery market is driven by several factors. One of the important drivers is maximizing demand for the most efficient and effective price drug discovery process. 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.

Market Restraints

Despite the potential advantages of employing generative AI in drug discovery, there are a few restricting constraints that may hinder the rise of this sector. Firstly, the shortage of transparency in AI algorithms can impede securing regulatory approval. Regulators may not fully grasp how the AI systems are developing medication candidates, which might lead to uncertainty & potential safety concerns. In addition, the adoption of AI technology in drug discovery may be constrained by its high cost, especially for smaller biotech & pharmaceutical firms. It can be challenging to acquire and retain the highest quality datasets, which makes the requirement for large volumes of data to train AI models a challenge as well.

Market Opportunities

The pharmaceutical industry will significantly benefit from the application of generative AI in drug discovery. The time and cost of drug research can be considerably reduced by using AI to continually generate and screen prospective therapeutic candidates. It may also result in new options for treating a variety of diseases. Some of these illnesses are difficult to treat or have no cure at all. The usage of AI-generated data assists in discovering medication targets that were previously missed. This leads to the development of new therapeutic paths and drug classes. It may enhance patient outcomes and broaden the range of possible treatments. AI can be used for both personalized medicine and the discovery of new drugs. This is when a patient’s genetic makeup is used to tailor the medicine compositions.

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Report Segmentation of the Generative AI in Drug Discovery Market

Technology Insight

The deep learning segment is dominant in the technology segment in generative AI in the e-commerce market, with a market share of 36%. Owing to Deep learning is a subset of machine learning that focuses 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.

End-User Insight

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.

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Market Segmentation

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

Key Regions

• North America
• 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

Competitive Landscape

The market is very competitive, with some key players dominating it. Market players adopt various strategic initiatives, such as increasing production capacities and forming technology partnerships to gain an edge.

Market Key Players:

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

Recent Development of the Generative AI in Drug Discovery Market

• In 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.
• In 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.

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