2021 Artificial Intelligence (AI) In Drug Discovery Market is Booming: Who is Making Money, Who is Paying the Price?

(Heraldkeepers via Comtex)

The Artificial Intelligence (AI) In Drug Discovery market size is projected to reach USD XX Million by 2026, from USD XX Million in 2020 growing at a CAGR of 40.9% during 2021-2026.The research covers the current and historic Artificial Intelligence (AI) In Drug Discovery Market size and its growth trend with company outline of Key players/manufacturers: IBM Corporation, Google, NVIDIA Corporation, Microsoft, GNS Biotechnology, Deep Genomics, Cloud Pharmaceuticals, BioSymetrics, Berg Health, Insitro, and NuMedii among others.

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This global Artificial Intelligence (AI) In Drug Discovery market report identifies and analyses the emerging trends along with major drivers, challenges and opportunities in the industry with analysis on vendors, geographical regions, types, and applications. This Artificial Intelligence (AI) In Drug Discovery market study estimates the market status, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, and distributors. This report proves to be an indispensable document for every market enthusiast, policymaker, investor, and player. This global Artificial Intelligence (AI) In Drug Discovery market research report is a professional and a detailed report focusing on primary and secondary drivers, market share, leading segments and geographical analysis.

With the effectual use of technology, innovative applications and expertise, this Artificial Intelligence (AI) In Drug Discovery market research report has been generated which successfully manages bulky and complex market data tables. To give clear idea about the current and future marketplace, most up to date market insights and analysis has been offered via this report. Several critical market factors covered in this Artificial Intelligence (AI) In Drug Discovery Market report consist of primary research, benchmarking studies, secondary research, company profiles, competitive intelligence & reporting, syndicated research, data collection, data processing and analysis, survey design, and survey programming. Market research report execution is becoming very central for the businesses to gain success because it offers insights into revenue growth and sustainability initiative.

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The growing impetus for decreasing the cost of novel drug discovery and production in recent years is expected to bolster demand for artificial intelligence (AI) platforms in drug discovery space. According to industry journals, the average novel drug discovery and development cost are USD 2.6 billion. This is mainly due to the narrow development testing funnel which eliminates the majority of the candidate therapies within the preclinical and phase – 1 trial. Due to this, the adoption of artificial intelligence for a faster, efficient, and cost-effective drug discovery is gaining momentum amongst the pharmaceutical industry stakeholders.

Another key factor promoting the adoption of artificial intelligence in the drug discovery space is the monumental data generated by the molecule screening processes and the preclinical studies. The large data makes it a difficult process for the scientist to review all the scientific literature. In such cases, artificial intelligence can help boost up the screening process and reduce the time required for studying and identify drug molecule interactions and histology data.

Another key aspect of the rising adoption of AI in the drug discovery space is the availability of myriad options to carry out the standard processes such as data mining and customization capabilities of the artificial intelligence platforms. The advances in artificial intelligence such as machine learning and deep learning enable pharmaceutical companies to specifically recognize molecule binding properties of the drug with high accuracy.

Preclinical testing is one of the areas that account for high revenue loss and low returns. Optimizing preclinical testing can help reduce the cost. This is where artificial intelligence algorithm-based models can be used for accurate analysis of human physiology response as compared to animal models. AI-based models can help eliminate experimental costs and help in the efficient prediction of cross-species differences.

Major Classifications are as follows:

Artificial Intelligence (AI) In Drug Discovery Market: By Therapeutic Area

Oncology
Cardiovascular Diseases
Metabolic Diseases

Artificial Intelligence (AI) In Drug Discovery Market: By Component

Software
Service

Artificial Intelligence (AI) In Drug Discovery Market: By Technology

Machine Learning
Deep Learning

Artificial Intelligence (AI) In Drug Discovery Market: By Application

Neurodegenerative Diseases
Immuno-Oncology
CVD

Artificial Intelligence (AI) In Drug Discovery Market: By Geography

Europe
Germany
France
United Kingdom
Italy
Rest of Europe
Asia-Pacific
Japan
China
South Korea
India
Australia
Rest of Asia Pacific
North America
U.S.
Canada
South America
Brazil
Argentina
Rest of South America
Rest of the World
Middle East
Africa

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Artificial Intelligence (AI) In Drug Discovery Market : Report Chapters

Chapter 01 - Executive Summary

Chapter 02 - Market Overview

Chapter 03 - Key Market Trends

Chapter 04 - Key Success Factors

Chapter 05 - Global Artificial Intelligence (AI) In Drug Discovery Market – Pricing Analysis

Chapter 06 - Global Artificial Intelligence (AI) In Drug Discovery Market Demand (in Value or Size in US$ Mn) Analysis and Forecast, 2021-2026

Chapter 07 - Market Background

Chapter 08 - Global Artificial Intelligence (AI) In Drug Discovery Market Analysis () & Opportunity Assessment , By Product Type

Chapter 09 - Global Artificial Intelligence (AI) In Drug Discovery Market Analysis & Opportunity Assessment , by Distribution Channel

Chapter 10 - Global Artificial Intelligence (AI) In Drug Discovery Market Analysis & Opportunity Assessment , by Region

Chapter 11 - North America Artificial Intelligence (AI) In Drug Discovery market Analysis & Opportunity Assessment

Chapter 12 - Latin America Artificial Intelligence (AI) In Drug Discovery Market Analysis & Opportunity Assessment

Chapter 13 -Europe Artificial Intelligence (AI) In Drug Discovery market Analysis & Opportunity Assessment

Chapter 14 - East Asia Artificial Intelligence (AI) In Drug Discovery market Analysis and Forecast

Chapter 15 - South Asia Artificial Intelligence (AI) In Drug Discovery market Analysis and Forecast

Chapter 16 - Oceania Artificial Intelligence (AI) In Drug Discovery market Analysis and Forecast

Chapter 17 - MEA Artificial Intelligence (AI) In Drug Discovery market Analysis & Opportunity Assessment

Chapter 18 - Market Structure Analysis

Chapter 19 - Competition Analysis

Chapter 20 - Assumptions and Acronyms Used

Chapter 21 - Research Methodology

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