The Artificial Intelligence (AI) In Drug Discovery Global Market Report 2022-31 by The Business Research Company describes and explains the global artificial intelligence (AI) in drug discovery market and covers 2016 to 2021, termed the historic period, and 2022 to 2026, termed the forecast period, along with further forecasts for the period 2026-2031. The report evaluates the market across each region and for the major economies within each region.
The Artificial
Intelligence (AI) In Drug Discovery Global Market Opportunities And Strategies Report
covers AI in drug discovery market drivers, AI in drug discovery market trends,
AI in drug discovery market segments, AI in drug discovery market growth rate, AI
in drug discovery market major players, and AI in drug discovery market size.
View more on the Artificial Intelligence (AI) In Drug
Discovery Market Report:
The artificial
intelligence (AI) in drug discovery market report focuses particularly on
identifying the trends, opportunities, and strategies that can lead to success.
O&S reports are available off the shelf and can be delivered to clients on
the day of purchase.
The Artificial
Intelligence (AI) In Drug Discovery Global Market Opportunities And Strategies Report
is the most comprehensive report available on this market and will help gain a
truly global perspective as it covers 60 geographies in detail. The chapter on
the impact of COVID-19 gives valuable insights on supply chain disruptions,
logistical challenges, and other economic implications of the virus on the
market. The chapter also covers markets which have been positively affected by
the pandemic.
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A key AI in
drug discovery market trend:
Artificial
Intelligence tools are now widely used for molecular target identification and
selection in pharmaceutical industry. Target identification and selection is
defined as discovering a possible molecular target with a specific biological
action which is anticipated to have a defined therapeutic activity. These
molecular targets are either genes proteins or small molecules which further
require evaluation of functional genomics, structural genomics, proteomics, and
cell-based assays (in-vitro), animal research (in-vivo) assays. AI uses a large
drug information bank which includes drug candidates, gene expression
protein-protein interactions and clinical data records from publicly available
resource for anticipating the therapeutic potential. In addition, AI platforms
also demonstrate detailed spatial 3D structure of proteins and molecular
complexes with the help of training computer vision and machine learning models
on cryo-EM microscope data (2D structure). Quantitative Structure Activity
Relationship (QSAR) modeling tools have evolved in AI based QSAR approach to
identification of potential drug candidate by leveraging, linear discriminant
analysis (LDA), support vector machines (SVMs), random forest (RF) and decision
trees analysis.
The report
covers the artificial intelligence in drug discovery market segments:
1) By Technology: Deep Learning, Machine Learning
2) By Drug Type:
Small Molecule, Large Molecules
3) By Disease
Type: Metabolic Disease, Cardiovascular Disease, Oncology, Neurodegenerative
Diseases, Others
4) By End-Users:
Pharmaceutical Companies, Biopharmaceutical Companies, Academic and Research
Institutes, Others
Table Of
Contents
1. Artificial
Intelligence (AI) In Drug Discovery Market Executive Summary
2. Table of
Contents
3. List of
Figures
4. List of Tables
5. Report
Structure
.
21. Key Mergers
And Acquisitions In The Artificial Intelligence (AI) In Drug Discovery Market
22. Artificial
Intelligence (AI) In Drug Discovery Market Opportunities And Strategies
23. Artificial
Intelligence (AI) In Drug Discovery Market, Conclusions And Recommendations
24. Appendix
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