The report encompasses the forecast as well as analysis of the AI in the Drug Discovery market on a global and regional level. The study displays historical data of 2016 to 2019 along with a forecast from 2020 to 2026 based on revenue (USD Million). Additionally, the market growth drivers, opportunities, restraints, and trends are also included in the AI in the Drug Discovery market report.
We have encompassed industry analysis models in our report and extensively demonstrated the key business strategies and competitive landscape of the AI in the Drug Discovery market in our study.
Our study also includes an analysis of Porter’s Five Forces framework for understanding the competitive strategies used by market competitors. It also encompasses PESTLE analysis and SWOT analysis.
The report also offers an in-depth analysis of the market share of each industry player and gives an outline of the market position of key players in the AI in the Drug Discovery market. Moreover, the study offers wide coverage of key strategic improvements witnessed in the market such as acquisitions & mergers, new product launches, agreements, partnerships, collaborations & joint ventures, R&D activities, and geographical expansion of key players of the AI in Drug Discovery market.
The study provides a decisive view of the AI in the Drug Discovery market by segmenting the AI in the Drug Discovery market based on drug type, technology, end-user, therapeutic area, and regions. All the segments and sub-segments have been examined based on historic, current and future trends in the global market and the market size is estimated from 2020 to 2026 in terms of value. The regional segmentation includes the current and forecast demand for North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa.
An AI in Drug Discovery is used to identify targets, in silico drug design, drug development, predictive analytics, research risk assessment, clinical tracking and even more.
Machine learning may help maximize treatment by combining biological and clinical evidence with mathematical models and can be used to develop drug testing and probabilistic treatment applications. Computational deep learning research indicates this methodology should be helpful in finding complex models in results. Since biological and clinical data are diverse, deterministic quantic machine learning algorithms offer a reasonable opportunity to advance interpret them. However, one of the major challenges in the drug discovery phase is patient health.
The global AI in the Drug Discovery market is segmented into drug type, technology, end-user, therapeutic area, and region. On the basis of drug type, the global AI in the Drug Discovery market is classified as a small molecule and large molecule. The small molecule segment is expected to record the fastest growth over the predictable period, due to its advantages over the large molecule. On the basis of technology, the global AI in the Drug Discovery market is bifurcated into deep learning, machine learning and others where machine learning is more dominating over the predictable period. On the basis of end-user, the global AI in the Drug Discovery market is divided into pharmaceutical companies, biopharmaceutical companies, academic, and research institutes and others where pharmaceutical companies are leading. The global AI in the Drug Discovery industry is categorized into metabolic diseases, cardiovascular diseases, immuno-oncology, neurodegenerative diseases and others where oncology is at the front, depending on therapeutic areas.
Key players operating in the AI in the Drug Discovery industry are Atomwise Inc, BenevolentAI, Bio age, 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.
Key Insights from Primary Research
Key Recommendations from Analysts
Market Attractiveness-By Therapeutic Area
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