Being a proficient and all-inclusive   North America Artificial Intelligence (AI) in Drug Discovery  Market  report puts a light on primary and secondary drivers, market share, leading segments, possible sales volume, and geographical analysis. This market research report is a significant source of information about the industry, important facts and figures, expert opinions, and the newest developments across the globe. This report has reviews about key players in the market, major collaborations, merger and acquisitions along with trending innovation and business policies.

 

Furthermore, the world North America Artificial Intelligence (AI) in Drug Discovery Market report deeply analyses the potential of the market with respect to current state of affairs and the future prospects by considering all aspects of Proprietary HMI (Human Machine Interface) Software Market industry. Not to mention, in this competitive market place, market research report has a very central role to play by offering important and consequential market insights for the business. The market drivers and restraints have been explained using SWOT analysis. With an absolute devotion and commitment, Europe North America Artificial Intelligence (AI) in Drug Discovery market report has been provided with the best reasonable service and recommendations which can be relied upon confidently.

 

Data Bridge Market Research analyzes that the North America Artificial Intelligence (AI) in drug discovery market is expected to reach the value of USD 14,913.08 million by 2029, at a CAGR of 54.9% during the forecast period. Software accounts for the largest technology segment in the market due to rapid developments in technological advancements to commercialize the use of AI in drug discovery market. This market report also covers pricing analysis, patent analysis, and technological advancements in depth.       

 

 

 

AI has caught the attention and minds of medical technology practitioners in the past few years, as several companies and major research laboratories have worked to perfect these technologies for clinical use. The first commercialized demonstrations of how AI (also known as Deep Learning (DL), Machine Learning (ML), or Artificial Neural Networks (ANNs) could assist clinicians are now available. These systems could lead to a paradigm shift in clinician workflow, and increase productivity while simultaneously enhancing treatment and patient throughput. AI for drug discovery is a technology that uses machines to simulate human intelligence to solve complicated challenges in the drug development procedure. 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. In the pharmaceutical sector, it aids in the discovery of novel compounds, therapeutic target identification, and the development of customized medications. AI platforms used for drug discovery can prove to be a feasible option for deriving insights into the discovery of drugs to treat and minimize the severity of various chronic diseases.

 

Some of the key players operating in the market are NVIDIA Corporation, IBM Corp., Atomwise Inc., Microsoft, Benevolent AI, Aria Pharmaceuticals, Inc., DEEP GENOMICS, Exscientia, Cloud, Insilico Medicine, Cyclica, NuMedii, Inc., Envisagenics, Owkin Inc., BERG LLC, Schrödinger, Inc., XtalPi Inc. and BIOAGE Inc. among others.

 

 

North America Artificial Intelligence (AI) in drug discovery market is segmented into application, technology, drug type, offering, indication, and end use. The growth among segments helps you analyze niche pockets of growth and strategies to approach the market and determine your core application areas and the difference in your target markets.

 

 

 

APPLICATION

 

 

 

Novel Drug Candidates

 

Drug Optimization and Repurposing Preclinical Testing and Approval

 

 Drug Monitoring

 

 Finding New Diseases Associated Targets and Pathways

 

 Understanding Disease Mechanisms

 

 Aggregating and Synthesizing Information

 

 Formation & Qualification of Hypotheses

 

 De Novo Drug Design

 

 Finding Drug Targets of an Old Drug

 

Others

 

Based on application, the market is segmented into novel drug candidates, drug optimization and repurposing preclinical testing and approval, drug monitoring, finding new diseases associated targets and pathways, understanding disease mechanisms, aggregating and synthesizing information, formation & qualification of hypotheses, de novo drug design, finding drug targets of an old drug, and others.

 

 

 

TECHNOLOGY

 

 

 

Machine Learning (ML)

 

Deep Learning (DL)

 

Natural Language Processing (NLP)

 

Others

 

Based on technology, the market is segmented into Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and others.

 

 

 

Part 01: Executive Summary

 

Part 02: Scope of the Report

 

Part 03: Research Methodology

 

Part 04: Market Landscape

 

Part 05: Pipeline Analysis

 

Part 06: Market Sizing

 

Part 07: Five Forces Analysis

 

Part 08: Market Segmentation

 

Part 09: Customer Landscape

 

Part 10: Regional Landscape

 

Part 11: Decision Framework

 

Part 12: Drivers and Challenges

 

Part 13: Market Trends

 

Part 14: Vendor Landscape

 

Part 15: Vendor Analysis

 

Part 16: Appendix

 

 

 

 

 

 

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