by Artificial Intelligence in Drug Discovery Market is estimated to be valued at US$ 1266.7 Mn in 2023 and is expected to exhibit a CAGR of 6.9% over the forecast period 2023 to 2030, as highlighted in a new report published by Coherent Market Insights.
Drug discovery is a complex process that involves screening of thousands of compounds to identify potential drug candidates. Artificial intelligence techniques such as machine learning and deep learning are being increasingly used in drug discovery to speed up the research and development process and reduce costs. AI algorithms can analyze huge amounts of data from research, clinical trials, and other sources to identify patterns and discover new drug targets. This enables drug makers to focus their resources on the most promising drug candidates with a higher chance of success. The use of AI is also accelerating preclinical and clinical drug discovery workflows by powering tasks such as compound screening, molecular modeling, and simulating clinical trials.
Market key trends:
The adoption of artificial intelligence in drug discovery is being propelled by the increasing demand for AI-accelerated drug discovery processes. AI systems can analyze massive datasets much faster than humans to identify biological relationships and hidden patterns. This helps researchers explore potential treatments more efficiently and focus resources on the most promising drug candidates. For instance, machine learning algorithms can analyze thousands of chemical compounds screened in vitro to identify the ones with best efficacy and safety profiles for advancing to in vivo testing. This capability of AI to speed up early-stage research is leading more pharmaceutical and biotech companies to adopt these technologies for internal drug discovery programs as well as partnering with AI drug discovery startups.
SWOT Analysis:
Strength: Artificial intelligence can analyze large datasets and identify patterns faster than humans. This helps in reducing drug discovery time and costs.
Weakness: Lack of explainability of results from AI/ML models is a challenge for drug discovery. High investment and expertise required to develop AI solutions.
Opportunity: AI can be used to accelerate virtual screening, target identification, predictive toxicology etc. Growing focus on precision medicine offers opportunities.
Threats: Data privacy and security concerns. Reliance on data can be a threat if data quality or volume is not adequate. Competitive pressure from startups.
Key Takeaways:
The Global Artificial Intelligence In Drug Discovery Market Share is expected to witness high growth.
Regional analysis: North America region currently dominates the market due to large presence of AI solution providers and pharmaceutical R&D spending. However, Asia Pacific region is expected to grow at the fastest pace owing to increasing government initiatives, growing healthcare expenditure and presence of major generics manufacturers in the region.
Key players: Key players operating in the Artificial Intelligence in Drug Discovery market are Lenzing A.G., Aditya Birla Group, AkzoNobel N.V., Smartfiber AG, Nien Foun Fiber Co., Ltd., Invista , Baoding Swan Fiber Co. Ltd., Qingdao Textiles Group Fiber Technology Co., Ltd., China Bambro Textile (Group) Co., Ltd., Acegreen Eco-Material Technology Co. Ltd., China Populus Textile Ltd., and Acelon Chemicals & Fiber Corp.
Note:
Source: Coherent Market Insights, Public sources, Desk research
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