(AI) Artificial Intelligence in Drug Discovery Market to Rise at 33.9% CAGR, to Reach USD 5,558.0 Million by 2029: Polaris Market Research

According to the latest research study, the report predicts the global size & share of Artificial Intelligence in Drug Discovery Market to reach around USD 5,558.0 Million by 2029 from USD 626.6 Million in 2021. The global Artificial Intelligence in Drug Discovery Market is anticipated to register a significant CAGR of 33.9% by 2029.

According to the latest research study, the report predicts the global size & share of Artificial Intelligence in Drug Discovery Market to reach around USD 5,558.0 Million by 2029 from USD 626.6 Million in 2021. The global Artificial Intelligence in Drug Discovery Market is anticipated to register a significant CAGR of 33.9% by 2029.

More encouraging aims for the outline of new drugs are recognized than ever before because of an explosion of knowledge about the molecular mechanism of several diseases. The main objective of drug discovery research is to acknowledge medicines that emanate a favorable impact on the body. They are medications that assist in prohibiting a disease that is specific. AI in drug discovery defeats the impediments by discovering new exchange and regulating the effective gravity of several cellular pathway components.

AI uses synthesized algorithms and machine learning to bring out information that is meaningful from extensive data sets. For instance, AI uses RNA sequencing data to recognize genes whose proclamation corresponds with a given cellular state. The advantage of AI in drug discovery is that AI does not depend on targets that are predetermined. Therefore, a bias that is personal ad former knowledge that abstains from any role in drug development.

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Includes updated regional scrutinizing with the graphical representation of share, size, and trends.
The report offers top market players their sales volume, business strategy, and revenue analysis

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Key Offerings:

Market size and forecast by revenue (2022β€”2029)
Market dynamics – Prime trends, restraints, growth drivers, and opportunities of investment
Market segmentation – A detailed analysis by source, product, distribution channel, and region
Competitive landscape – vendors that are crucial and topmost

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Important Questions Being Answered by The Market Report

What is the market size and growth?
What kind of product offering comprises the most significant share of artificial intelligence in drug discovery market?
Which type of technology is extensively acquired for artificial intelligence in drug discovery?
Which is the central application portion?
Which is the prominent end-user?
Which region will make notable contributions to the market?
Which are the significant players capitalizing on global AI in the drug discovery market?

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Top Market Players Includes:

Atomwise
Benevolent AI
Berg Health
BIOAGE
BioSymetrics
Cloud Pharmaceuticals
Cyclica
Deep Genomics
DeepMind
Envisagenics
Euretos
Exscientia
GNS Healthcare
IBM Corporation
Insilico Medicine
Insitro
NuMedii
Numerate
NVIDIA Corporation
OWKIN Inc.
Aria Pharmaceuticals Inc.
Verge Genomics
XtalPi Inc

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Artificial Intelligence in Drug Discovery Market: Growth Leading Factors

The AI for drug discovery is seeing speedy growth because of an increase in cross-industry alliances and cooperation. Additionally, the change in the significance of AI and drug discovery in advancement and expansion in the financing of R&D activities for the usage of AI technology is anticipated to fuel the market growth. Another essential feature of the growing acquisition of AI in the space of drug discovery is the obtainability of the host of options to execute the customary procedures like customization capabilities and data mining for artificial intelligence platforms. The progress in AI like deep and machine learning sanction pharmaceutical companies to identify properties of molecule binding of the drug with increased precision.

Preclinical testing is one of the fields that explain revenue loss that is high and returns that are low. Enhancing preclinical testing can assist in lowering the costs. This enhancement is precisely why AI algorithm-based models are utilized. They can exactly analyze human physiology response when contrasted to animal models. AI-dependent models can assist in doing away with fact-finding costs and aid in the prediction that is efficient of differences in cross-species.

In the covid-19 pandemic, the usage of AI could boost the growth of the market. Due to the forecast of the protein structure, scientists can better analyze viruses and help in the vaccine development that can activate an immune response. These structures of the protein are being utilized by researchers to check the functions of coronavirus and help the hypothesis generation systems for laboratory work in the future in finding therapeutics.

Global Artificial Intelligence in Drug Discovery Market: Report Scope

Report Attribute

Details

Market Size 2021 Value

USD 626.6 Million

Market Outlook for 2029

USD 5,558.0 Million

Expected CAGR Growth

33.9% from 2022 – 2029

Base year

2021

Forecast period

2022 – 2029

Segments covered

By Application, By Therapeutic Area, By Offering, By Technology, By Region

Key Companies

Atomwise, Benevolent AI, Berg Health, BIOAGE, BioSymetrics, Cloud Pharmaceuticals, Cyclica, Deep Genomics, DeepMind, Envisagenics, Euretos, Exscientia, GNS Healthcare, IBM Corporation, Insilico Medicine, Insitro, NuMedii, Numerate, NVIDIA Corporation, OWKIN, Inc., Aria Pharmaceuticals, Inc., Verge Genomics, XtalPi Inc.

Regional scope

North America, Europe, Asia Pacific, Latin America, Middle East & Africa

Customization Options

Customized purchase options are available to meet your research needs.

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Artificial Intelligence in Drug Discovery Market: Key Segmentation

Insight by application, therapeutic area, offering, technology, and region

Based on application, the market is segmented into candidate screening, target recognition, drug optimization, and preclinical testing. The applicant screening division is said to have the largest share. By the therapeutic area, the immuno-oncology group is estimated to dominate the market. In precision medicine, AI technology is displaying assurance in transforming genetic mutations. By offering, the software division has the largest market share. The two of the most popular AI platforms are Google AI and Microsoft Azure. By technology, machine learning involves a gamut of techniques that can assist people in making better decisions as they possess an ample amount of good data. This use of machine learning will help in lessening failure rates.

By region, America dominates the market as the area is rapidly growing in the drug development market with the increasing number of extensive AI platform developers. Big pharma firms utilize some of the topmost AI tools like Microsoft Azure, Google AI, and TensorFlow for the drug development process. Europe is the second-largest market for AI drug discovery. Increasing R&D initiatives in the pharmaceutical industry and robust demand for AI solutions from large pharma firms are estimated to push the regional market ahead.

Browse the full β€œArtificial Intelligence in Drug Discovery Market Share, Size, Trends, Industry Analysis Report, By Application (Drug Optimization & Repurposing, Preclinical testing, Others); By Therapeutic Area; By Offering; By Technology; By Region; Segment Forecast, 2022 – 2029” Report at https://www.polarismarketresearch.com/industry-analysis/ai-in-drug-discovery-market

The market predominantly depends on the application, therapeutic area, offering, technology, and region

Artificial Intelligence in Drug Discovery Market: By Application

Drug optimization & repurposing
Preclinical testing
Others

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Artificial Intelligence in Drug Discovery Market: By Therapeutic Area

Oncology
Neurodegenerative Diseases
Cardiovascular Disease
Metabolic Diseases
Infectious Disease
Others

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Artificial Intelligence in Drug Discovery Market: By Offering

Software
Services

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Artificial Intelligence in Drug Discovery Market: By Technology

Machine Learning
Deep Learning
Supervised Learning
Reinforcement Learning
Unsupervised Learning
Other Machine Learning Technologies
Other Technologies

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