by The global Artificial Intelligence in Drug Discovery Market is estimated to be valued at US$ 1,266.7 Mn or in 2023 and is expected to exhibit a CAGR of 5.7% over the forecast period 2023 to 2030, as highlighted in a new report published by Coherent Market Insights.
Market Overview:
Artificial intelligence (AI) in drug discovery uses machine learning and deep learning algorithms and techniques to analyze large amounts of chemical, biological and genomic data in order to discover new drug candidates. AI techniques such as deep learning, machine learning and natural language processing are being used across various areas of drug discovery including target identification and validation, lead identification, lead optimization and clinical trials. The increasing adoption of AI in drug discovery offers significant advantages over traditional drug discovery methods such as reducing time, cost and improving accuracy and effectiveness of the process.
Market key trends:
One of the major trends in the artificial intelligence in drug discovery market is increasing investment in AI startups. Venture capital firms and large pharmaceutical companies are making significant investments in AI startups which are deploying cutting edge technologies such as deep learning and machine learning to speed up and optimize the drug discovery process. For instance, according to CB Insights, funding to AI healthcare startups increased from US$ 1.5 billion in 2016 to US$ 9.3 billion in 2021. Key players in the market such as IBM Corporation (IBM Watson Health), Exscientia, GNS Healthcare, Alphabet, Inc. (DEEPMIND), Benevolent AI, Biosymetrics, Euretos, Berg LLC., Atomwise, Inc., Insitro are focusing on strategic collaborations and new product launches to enhance their market share in the AI in drug discovery market.
Porter’s Analysis
Threat of new entrants: The threat is medium as setting up AI-enabled drug discovery capabilities require substantial R&D investments and data access. However, accessibility to cloud computing is lowering the entry barriers.
Bargaining power of buyers: The bargaining power of buyers is medium as the key pharmaceutical companies investing in AI drug discovery have significant influence. However, growing number of small biotech firms provide options to buyers.
Bargaining power of suppliers: Data and AI platform providers have some bargaining power. However, competition among various suppliers has ensured relatively fair prices.
Threat of new substitutes: Threat from new substitutes is low as AI is opening new frontiers in drug discovery with potential to address challenges faced by traditional methods.
Competitive rivalry: Intense as major tech and pharma players are investing heavily to gain early mover advantages and build differentiated AI platforms for drug discovery.
Key Takeaways
The Global Artificial Intelligence in Drug Discovery Market Share is expected to witness high growth, exhibiting 5.7% CAGR over the forecast period, due to increasing pressure on pharma players to accelerate drug discovery amid rising R&D costs.
Regional analysis: North America dominated the market and is expected to continue its dominance over the forecast period due to presence of major players and growing investments by pharma and tech giants in the region. However, Asia Pacific is anticipated to register the fastest growth rate supported by government initiatives, increasing investments by key players and growing biotech industry in the region.
Key players: IBM Corporation (IBM Watson Health), Exscientia, GNS Healthcare, Alphabet, Inc. (DEEPMIND), Benevolent AI, Biosymetrics, Euretos, Berg LLC., Atomwise, Inc., Insitro, and among others. Key players operating in the Artificial Intelligence in Drug Discovery are focusing on developing specialized AI platforms and tools to accelerate drug discovery process. Players are also collaborating with biopharma companies and research institutes to gain real-world data access and domain expertise.
*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
