Evolution of the Global Machine Learning in Pharmaceutical Industry Market: Anticipated to Garner $26.2 billion by 2031

A report published by Allied Market Research on global machine learning in the pharmaceutical industry market states that the sector is projected to display a noteworthy CAGR of 37.9% with an absolute revenue of $26.2 billion by 2031. The market garnered $1.2 billion in 2021.  The report offers a brief analysis of key investment pockets, market size, dynamics, competitive scenario, drivers, and important strategies.

Key Takeaways

• On the basis of enterprise size, the sector is divided into the large enterprise and SMEs segments.
• The large enterprise segments gained the highest revenue in 2021.
• As per regional analysis, North American machine learning in the pharmaceutical industry market garnered a major share in 2021.

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Prominent factors influencing the market expansion

Rising usage of ML (Machine Learning) algorithms in the pharmaceutical industry

In the pharmaceutical sector, machine learning (ML) is gaining traction as a tool to assist in the process of drug discovery and development. ML methodologies are utilized to study vast datasets including genomics, proteomics, and metabolomics information by simplifying the identification of novel drug targets and promising drug candidates.

Moreover, ML algorithms offer the ability to forecast the efficacy of prospective drug candidates and assist researchers in prioritizing which candidates warrant further exploration in the drug development pipeline. Such predictive abilities can streamline the process and conserve valuable time and resources by minimizing the volume of candidates for clinical trials. Due to these features, the global machine learning in pharmaceutical market is gaining momentum.

Segmental analysis of the industry

Component

Solution: In the Machine Learning in Pharmaceutical Industry Market, the solution segment involves using machine learning algorithms for the analysis of large datasets across various domains including drug discovery, clinical trials, personalized medicine, and beyond. Machine learning streamlines clinical trials, formulates treatment strategies, refines drug discovery methodologies, and improves patient outcomes. It enables the identification of patterns and insights within vast amounts of data, leading to more efficient processes and personalized healthcare solutions.

Services: The services segment involves analyzing electronic medical records to find appropriate trial participants by predicting participant ratio to avoid complications. This optimizes trial site performance and data quality using AI/ML-assisted monitoring tools.

Enterprise size

SMEs: ML algorithms have the capacity to analyze vast datasets of known drug molecules and chemical databases, identifying patterns and features correlated with effective drugs. This ability streamlines potential drug candidate screening by accelerating the discovery process, even for smaller and medium-sized companies with limited resources. Moreover, statistical analyses of manufacturing data can be used to optimize processes, increase quality, and reduce risk by improving consumer safety. This can help SMEs in improving their industrial operations.

Large enterprises: Large pharmaceutical companies can utilize machine learning technology to analyze vast amounts of data from diverse sources such as clinical trials, electronic health records, and genetic information. This enables them to pinpoint potential drug targets, forecast patient outcomes, and refine clinical trial designs. With substantial resources and expertise, these companies can internally develop and integrate machine learning solutions or acquire specialized firms in the field.

Deployment

Cloud: Cloud computing offers a scalable and cost-effective platform for storing and analyzing vast datasets, vital for machine learning applications in drug discovery and development. By leveraging the cloud, pharmaceutical companies can tap into powerful computational resources and advanced algorithms without the requirement for substantial investment in hardware or software infrastructure. Moreover, cloud technology facilitates seamless collaboration and data sharing among teams and organizations, accelerating the pace of drug discovery and development.

On-premise: Opting for on-premise services can result in greater capital savings compared to cloud services. This is because maintaining a machine learning process in a public cloud on a pay-as-you-go basis can become expensive due to the intensive utilization and distribution of data. Therefore, on-premise deployment emerges as a more cost-effective option. In the Pharmaceutical Industry, the integration of on-premise solutions in Machine Learning optimizes processes, effectively manages data, and ensures cost efficiency.

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Questions covered in the report

What are the key technological trends shaping the market?

What is the market size and growth rate of the market for selective regions?

Which key players are profiled in the report?

Top entities

Frontrunners in the industry are:

• Deep Genomics
• NVIDIA Corporation
• International Business Machines Corporation
• cyclica inc.
• Atomwise Inc.
• BioSymetrics Inc.
• IBM
• Cloud Pharmaceuticals, Inc.
• Alphabet Inc.
• Microsoft Corporation

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Regional landscape

The global machine learning in pharmaceutical industry market is assessed across various regions including LAMEA, Europe, Asia-Pacific, and North America. North America generated the highest revenue in 2021. In recent times, there has been a notable surge in the integration of machine learning technologies in the North American industry to enhance operational efficiency, accelerate the processes of drug discovery and development, and promote innovation.

Recent developments

Data analysis and insights

ML is used to process and analyze vast datasets from the pharmaceutical sector, comprising genomic data, clinical trial data, and electronic health records. This assists in identifying crucial patterns and insights necessary for informing decisions in drug development.

Optimized in clinical trials

ML is revolutionizing the landscape of clinical trial design, operations, and analysis. The use of AI/ML techniques in clinical trials is becoming common due to the growing reliance on digital technology for patient data collection.

About AMR

AMR’s reports provide a detailed examination of every aspect of the market, from emerging trends to market dynamics, regulatory landscapes, and competitive analysis. With comprehensive coverage, we empower businesses with the knowledge to make informed decisions, evaluate potential investments, and refine business strategies.

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