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.

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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
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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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