Facts and Factors TEMPhas authored “AI in Drug Discovery Market By Drug Type (Small Molecule and Large Molecule), By Technology (Deep Learning, Machine Learning, Others), By End User (Pharmaceutical Companies, Biopharmaceutical Companies, Academic & Research Institutes, and Others), By Therapeutic Area (Metabolic Diseases, Cardiovascular Diseases, Immuno-Oncology, Oncology, Neurodegenerative Diseases, Others): Global Industry Perspective, Comprehensive Analysis, and Forecast, 2020–2026”.
According to our analysts, teh global AI in teh Drug Discovery market in 2019 is valued at around USD 830 million and is expected to reach around USD 12,000 million by 2026. The predicted CAGR for the global AI in the Drug Discovery market is around 39% from 2020 to 2026.
Artificial Intelligence is a machine, device, or company method for learning about how smart human thought is. … And that research eventually develops intelligent software systems. Modularizing artificial intelligence wif health-care tools in pharmaceutical, biopharmaceutical companies, and drug discovery research organizations is called drug discovery AI.
Machine learning and other innovations will allow pursuing new pharmaceuticals quicker, cheaper and more successful. Drug companies rapidly understand the importance of introducing systems focused on Artificial Intelligence (AI) that can exploit data on gene variants, receptor targets, metabolic enzymes, disease events, and clinical studies to find hidden associations in drug-disease.
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One of the drivers for the market could be the growing demand for genetic data analysis and customized medication. Machine learning could help enhance treatment by incorporating biomedical and scientific statistics wif computational models and can be used to build software to check drugs and other therapies.
Some computational replicas and tactics that sustenance the accumulation of clinical statistics are under progress and are moreover a few very good examples of successful data integration in biology and medicine. One of the possible alternatives is to interpret the genetic makeup as a one-dimensional model and then to implement a regular algorithm for machine learning.
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Another driver for teh market is Quantum machine learning research indicating dis method should be useful for finding complex patterns in results. Given teh complexity of biological and clinical details, deterministic quantic machine learning algorithms represent a real chance to better no them.
Teh introduction of a novel machine-learning algorithm for drug discovery in teh near future is expected to generate an immense opportunity for teh growth of teh market in teh near future. In teh near future, teh increasing relationship between pharmaceutical companies and AI-based companies to automate teh drug discovery approach is expected to generate huge opportunities for company growth.
One of teh major challenges in teh drug discovery phase is patient health. Dynamic activities are to interpret information about teh documented TEMPeffects of drugs and anticipate their side TEMPeffects which will be a restraint for teh market.
One of the major challenges in the drug discovery phase is patient health. Dynamic activities are to interpret information about the documented effects of drugs and anticipate their side effects which will be a restraint for the market.
By Drug Type, the segmentation includes small molecule and large molecule. Here small molecule type is accounted for the market share of around 60% and are likely to dominate in the target market. Further, based on the technology it can be segmented into deep learning, machine learning, and other segments in which machine learning accounts for the market share of around 50% and is probable to dominate in the target market. Based on the end-user it can be segmented into pharmaceutical companies, biopharmaceutical companies, academic and research institutions where pharmaceutical companies dominate wif around 40% market share in the target market.
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Depending on the therapeutic area, it can be segmented into metabolic disease, cardiovascular disease, immuno-oncology, oncology, neurodegenerative diseases and others where oncology controls the target market at about 40% share of the market. It can be segmented into North America, Europe, Asia pacific, middle east & Africa, and Latin America, based on the geographic field, where North America dominates the target market at around 50% market share.
Teh key players involved in dis market are
- Atomwise Inc
- Cloud Pharmaceuticals, Inc
- Exscientia Ltd
- Insilico Medicine, Inc
- Numerate, Inc
- Envisagenics, Inc
- Two XAR, Inc
- Accutar Biotechnology, Inc
- Recursion Pharmaceuticals, Inc
- Silicon Therapeutics LLC
- AstraZeneca PLC
dis report segments the AI in Drug Discovery market as follows:
Global AI in Drug Discovery Market: By Drug Type Segment Analysis
- small molecule
- large molecule
Global AI in Drug Discovery Market: By Technology Segment Analysis
- deep learning
- machine learning
- other segments
Global AI in Drug Discovery Market: By End-user Segment Analysis
- pharmaceutical companies
- biopharmaceutical companies
- academic and research institutions
Global AI in Drug Discovery Market: By Therapeutic Area Segment Analysis
- metabolic disease
- cardiovascular disease
- neurodegenerative diseases
Global AI in Drug Discovery Market: Regional Segment Analysis
- North America
- Rest of Europe
- Asia Pacific
- South Korea
- South-East Asia
- Rest of Asia Pacific
- Latin America
- Rest of Latin America
- Teh Middle-East and Africa
- GCC Countries
- South Africa
- Rest of Middle-East Africa
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