Artificial Intelligence in Drug Discovery Industry Overview
The global artificial intelligence in drug discovery market size was valued at USD 897.6 million in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 29.4% from 2022 to 2030. The growing demand for the discovery and development of novel drug therapies and increasing manufacturing capacities of the life science industry are driving the demand for artificial intelligence (AI) empowered solutions in the drug discovery processes. Manufacturers in the life science industry constantly focus on replenishing their product pipelines as the majority of the big sellers go off patent. Furthermore, a growing number of public-private partnerships boosting the adoption of AI-powered solutions in drug discovery and development processes is driving the market. Countries such as France, the U.S., Spain, and Japan dominate the clinical trial space, while the U.K. is focused on enhancing research development activities.
Drug discovery and development is a cost-intensive and time-consuming process. As per the data published in industry journals, the average costs of discovering and developing novel drug therapies are USD 2.6 billion, and the time period of more than 10 years. Most of the candidate therapies are eliminated within the initial phases of the clinical trial, specifically preclinical and phase-1 trials owing to the narrow development testing funnel, which directly contributes to the high costs and vast timelines involved in the process.
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The adoption of AI solutions in the clinical trial process eliminates possible obstacles, reduces clinical trial cycle time, and increases the productivity and accuracy of the clinical trial process. Therefore, the adoption of these advanced AI solutions in drug discovery processes is gaining popularity amongst life science industry stakeholders. According to Clinical Trials Arena data estimates in 2021, the strategic collaborations and partnerships between major AI-based drug discovery companies and pharmaceutical companies increased from 4 partnerships in 2015 to 27 partnerships in 2020.
Digitalization in biomedical and clinical research spaces is paving a path for the implementation of AI solutions. The wide array of datasets generated from drug discovery processes such as molecule screening phase and preclinical studies is boosting the adoption of AI-powered solutions. The monumental datasets make it difficult for researchers to analyze the studies accurately and through the implementation of AI solutions, the screening processes can be accelerated and the turnaround time can be reduced. Furthermore, the ongoing Covid-19 pandemic has dramatically changed the perception toward clinical trials and has increased the penetration and utilization of AI solutions. For instance, renowned pharmaceutical companies such as Pfizer, Novartis, Bayer, Sanofi, and Johnson Johnson are collaborating with AI-based drug discovery solutions providers.
The availability of myriad options such as data mining and personalization capabilities in adopting AI solutions in drug discovery processes is driving the market. Furthermore, enhanced accuracy is achieved through the integration of deep learning and machine learning algorithms in AI platforms in identifying molecule binding properties of the drug. Moreover, the incorporation of advanced technologies such as electronic data capture (EDC) supports manufacturers in improving patient data management and reduces monitoring costs. Process errors can be minimized by integrating electronic Clinical Outcome Assessment (e-COA) in the AI solutions. Lately, advanced analytics are being integrated into these advanced AI solutions, which aid stakeholders in data mining, patient recruitment, and medical and clinical records management.
Amongst the different phases of a clinical trial study, preclinical testing accounts for the highest revenue loss with low returns. Through the adoption of AI solutions, the preclinical testing phase can be optimized to minimize costs. AI-based models are implemented to accurately analyze human physiological responses and eliminate experimental costs. Stringent regulations pertaining to clinical trial studies laid down by regulatory authorities across the globe are anticipated to drive the demand for AI solutions in drug discovery processes. On the other hand, government authorities of various developed and emerging economies are undertaking favorable initiatives to increase the penetration of AI solutions and increase the number of clinical trials.
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Artificial Intelligence in Drug Discovery Market Segmentation
Grand View Research has segmented the global artificial intelligence in drug discovery market based on application, therapeutic area, and region:
Artificial Intelligence in Drug Discovery Application Outlook (Revenue, USD Million, 2016 - 2030)
- Drug Optimization and Repurposing
- Preclinical Testing
- Others
Artificial Intelligence in Drug Discovery Therapeutic Area Outlook (Revenue, USD Million, 2016 - 2030)
- Oncology
- Neurodegenerative Diseases
- Cardiovascular Diseases
- Metabolic Diseases
- Infectious Diseases
- Others
Artificial Intelligence in Drug Discovery Component Outlook (Revenue, USD Million, 2016 - 2030)
- Software
- Hardware
- Services
Artificial Intelligence in Drug Discovery Regional Outlook (Revenue, USD Million, 2016 - 2030)
- North America
- Europe
- Asia Pacific
- Latin America
- MEA
Key Companies profiled:
Some prominent players in the global Artificial Intelligence In Drug Discovery market include
- IBM Watson
- Exscientia
- GNS Healthcare
- Alphabet (DeepMind)
- Benevolent AI
- BioSymetrics
- Euretos
- Berg Health
- Atomwise
- Insitro
- Cyclica
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