Global AI Drug Target Discovery Service Market Growth (Status and Outlook) 2025-2031
Description
According to this study, the global AI Drug Target Discovery Service market size will reach US$ 14469 million by 2031.
AI Drug Target Discovery Service refers to a method that uses artificial intelligence technology to identify, validate and screen potential drug targets. By analyzing and mining massive biomedical data, this method uses machine learning, deep learning and other algorithms to predict and identify disease-related biomolecules, which can be used as targets for drug development, thereby accelerating the process of new drug discovery and research and development.
Market Opportunities and Key Drivers:
The AI Drug Target Discovery Service is experiencing unprecedented growth opportunities, with the core driving forces stemming from the triple resonance of technological innovation, policy support, and market demand. This growth is attributed to the disruptive impact of AI technology on the efficiency of drug development: traditional target discovery takes 5-7 years, while AI algorithms can shorten the cycle to 1-2 years, reducing the research cost by 40%-60%, and increasing the success rate of preclinical target validation from less than 10% to over 25%. At the policy level, China's "14th Five-Year Plan" has designated AI pharmaceuticals as a key development area, and the US FDA has also launched an "AI Priority" fast-track approval channel to accelerate the entry of AI-discovered targets into clinical trials. At the market demand end, the explosive growth in the treatment needs for chronic diseases and rare diseases has become the core driving force - for instance, AI successfully identified PD-L1's drug resistance mechanism-derived targets (such as LAG-3) in tumor target discovery, promoting the iteration of immunotherapy; in the rare disease field, AI analyzed the data from the 10,000 Genomes Project to identify new targets such as SMN2 enhancer for SMA (spinal muscular atrophy), filling the gaps in traditional research. Additionally, the capital enthusiasm continues to rise: in 2024, the global financing in the AI pharmaceutical field exceeded 12 billion US dollars, with target discovery enterprises accounting for 35% (such as Insilico Medicine, Exscientia, etc.), and pharmaceutical giants such as Eli Lilly and Roche have deeply invested in AI target platforms through cooperation or acquisition, further catalyzing industry expansion.
Challenges and Future Directions:
The AI Drug Target Discovery Service still faces multiple challenges in terms of data, technology, and ethics. The primary data bottleneck is the foremost issue: the fragmentation of biomedical data and privacy barriers lead to a lack of high-quality training data, for example, only 15% of protein interaction data related to target association meet the requirements of AI models, and the anonymization of patient genetic data weakens the accuracy of target prediction. The insufficient technical maturity also restricts application: deep learning models have poor explainability in predicting target mechanisms (the "black box problem"), resulting in 30% of AI predicted targets being unable to be verified through wet experiments; at the same time, the shortage of interdisciplinary talents separates algorithm development from biological validation, prolonging the transformation cycle. The regulatory and ethical risks are equally prominent: there are no unified standards for the intellectual property rights of AI-generated targets in various countries (such as whether AI can be listed as an inventor of a patent), and algorithm biases may ignore the characteristics of specific populations (such as the missing rate of genetic variation data for African Americans reaching 40%), exacerbating medical inequality. The future direction will focus on technology integration and ecosystem reconstruction: on the one hand, multimodal AI models (such as AlphaFold 3 combined with structure prediction and molecular dynamics simulation) can improve the accuracy of target screening, while quantum computing will solve the computational bottleneck of protein folding simulation; on the other hand, federated learning technology can integrate cross-border medical data under privacy protection, building a "global brain" for target discovery. At the industry level, the "AI + CRO" model (such as the collaboration between Wuxi Chemtech and Insilico) will promote the large-scale validation of targets, and blockchain technology may establish new standards for target data traceability and intellectual property rights confirmation. Ultimately, AI target discovery will evolve from a tool to a platform ecosystem, achieving a paradigm shift from "single-target breakthrough" to "disease network targeting" through open collaboration.
LPI (LP Information)' newest research report, the “AI Drug Target Discovery Service Industry Forecast” looks at past sales and reviews total world AI Drug Target Discovery Service sales in 2024, providing a comprehensive analysis by region and market sector of projected AI Drug Target Discovery Service sales for 2025 through 2031. With AI Drug Target Discovery Service sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world AI Drug Target Discovery Service industry.
This Insight Report provides a comprehensive analysis of the global AI Drug Target Discovery Service landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on AI Drug Target Discovery Service portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global AI Drug Target Discovery Service market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for AI Drug Target Discovery Service and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global AI Drug Target Discovery Service.
This report presents a comprehensive overview, market shares, and growth opportunities of AI Drug Target Discovery Service market by product type, application, key players and key regions and countries.
Segmentation by Type:
Target Identification Based on Omics Data and Biological Networks
Target Mining Based on Literature and Knowledge Graphs
Target Prediction Based on Structural Bioinformatics
Target Discovery Based on Phenotypic Screening and Virtual Patients
Segmentation by Application:
Pharmaceutical Company
CRO and Universities
Others
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
Exscientia
Atomwise
Benevolent AI
Insitro
Xaira Therapeutics
Tempus AI
AbCellera
Recursion
Iktos
Genialis
Anima Biotech
BPGbio
Cradle
Isomorphic Labs
Generate Biomedicines
Latent Labs
Relay Therapeutics
Model Medicines
Nimbus Therapeutics
Schrödinger
XtalPi
Insilico Medicine
Drug Farm
BioMap
Please note: The report will take approximately 2 business days to prepare and deliver.
AI Drug Target Discovery Service refers to a method that uses artificial intelligence technology to identify, validate and screen potential drug targets. By analyzing and mining massive biomedical data, this method uses machine learning, deep learning and other algorithms to predict and identify disease-related biomolecules, which can be used as targets for drug development, thereby accelerating the process of new drug discovery and research and development.
Market Opportunities and Key Drivers:
The AI Drug Target Discovery Service is experiencing unprecedented growth opportunities, with the core driving forces stemming from the triple resonance of technological innovation, policy support, and market demand. This growth is attributed to the disruptive impact of AI technology on the efficiency of drug development: traditional target discovery takes 5-7 years, while AI algorithms can shorten the cycle to 1-2 years, reducing the research cost by 40%-60%, and increasing the success rate of preclinical target validation from less than 10% to over 25%. At the policy level, China's "14th Five-Year Plan" has designated AI pharmaceuticals as a key development area, and the US FDA has also launched an "AI Priority" fast-track approval channel to accelerate the entry of AI-discovered targets into clinical trials. At the market demand end, the explosive growth in the treatment needs for chronic diseases and rare diseases has become the core driving force - for instance, AI successfully identified PD-L1's drug resistance mechanism-derived targets (such as LAG-3) in tumor target discovery, promoting the iteration of immunotherapy; in the rare disease field, AI analyzed the data from the 10,000 Genomes Project to identify new targets such as SMN2 enhancer for SMA (spinal muscular atrophy), filling the gaps in traditional research. Additionally, the capital enthusiasm continues to rise: in 2024, the global financing in the AI pharmaceutical field exceeded 12 billion US dollars, with target discovery enterprises accounting for 35% (such as Insilico Medicine, Exscientia, etc.), and pharmaceutical giants such as Eli Lilly and Roche have deeply invested in AI target platforms through cooperation or acquisition, further catalyzing industry expansion.
Challenges and Future Directions:
The AI Drug Target Discovery Service still faces multiple challenges in terms of data, technology, and ethics. The primary data bottleneck is the foremost issue: the fragmentation of biomedical data and privacy barriers lead to a lack of high-quality training data, for example, only 15% of protein interaction data related to target association meet the requirements of AI models, and the anonymization of patient genetic data weakens the accuracy of target prediction. The insufficient technical maturity also restricts application: deep learning models have poor explainability in predicting target mechanisms (the "black box problem"), resulting in 30% of AI predicted targets being unable to be verified through wet experiments; at the same time, the shortage of interdisciplinary talents separates algorithm development from biological validation, prolonging the transformation cycle. The regulatory and ethical risks are equally prominent: there are no unified standards for the intellectual property rights of AI-generated targets in various countries (such as whether AI can be listed as an inventor of a patent), and algorithm biases may ignore the characteristics of specific populations (such as the missing rate of genetic variation data for African Americans reaching 40%), exacerbating medical inequality. The future direction will focus on technology integration and ecosystem reconstruction: on the one hand, multimodal AI models (such as AlphaFold 3 combined with structure prediction and molecular dynamics simulation) can improve the accuracy of target screening, while quantum computing will solve the computational bottleneck of protein folding simulation; on the other hand, federated learning technology can integrate cross-border medical data under privacy protection, building a "global brain" for target discovery. At the industry level, the "AI + CRO" model (such as the collaboration between Wuxi Chemtech and Insilico) will promote the large-scale validation of targets, and blockchain technology may establish new standards for target data traceability and intellectual property rights confirmation. Ultimately, AI target discovery will evolve from a tool to a platform ecosystem, achieving a paradigm shift from "single-target breakthrough" to "disease network targeting" through open collaboration.
LPI (LP Information)' newest research report, the “AI Drug Target Discovery Service Industry Forecast” looks at past sales and reviews total world AI Drug Target Discovery Service sales in 2024, providing a comprehensive analysis by region and market sector of projected AI Drug Target Discovery Service sales for 2025 through 2031. With AI Drug Target Discovery Service sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world AI Drug Target Discovery Service industry.
This Insight Report provides a comprehensive analysis of the global AI Drug Target Discovery Service landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on AI Drug Target Discovery Service portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global AI Drug Target Discovery Service market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for AI Drug Target Discovery Service and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global AI Drug Target Discovery Service.
This report presents a comprehensive overview, market shares, and growth opportunities of AI Drug Target Discovery Service market by product type, application, key players and key regions and countries.
Segmentation by Type:
Target Identification Based on Omics Data and Biological Networks
Target Mining Based on Literature and Knowledge Graphs
Target Prediction Based on Structural Bioinformatics
Target Discovery Based on Phenotypic Screening and Virtual Patients
Segmentation by Application:
Pharmaceutical Company
CRO and Universities
Others
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
Exscientia
Atomwise
Benevolent AI
Insitro
Xaira Therapeutics
Tempus AI
AbCellera
Recursion
Iktos
Genialis
Anima Biotech
BPGbio
Cradle
Isomorphic Labs
Generate Biomedicines
Latent Labs
Relay Therapeutics
Model Medicines
Nimbus Therapeutics
Schrödinger
XtalPi
Insilico Medicine
Drug Farm
BioMap
Please note: The report will take approximately 2 business days to prepare and deliver.
Table of Contents
158 Pages
- *This is a tentative TOC and the final deliverable is subject to change.*
- 1 Scope of the Report
- 2 Executive Summary
- 3 AI Drug Target Discovery Service Market Size by Player
- 4 AI Drug Target Discovery Service by Region
- 5 Americas
- 6 APAC
- 7 Europe
- 8 Middle East & Africa
- 9 Market Drivers, Challenges and Trends
- 10 Global AI Drug Target Discovery Service Market Forecast
- 11 Key Players Analysis
- 12 Research Findings and Conclusion
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