Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI has numerous applications in various industries including healthcare. In healthcare, AI is widely used for diagnosing diseases, drug discovery, genomics research, and precision medicine. In drug discovery, AI helps identify new target molecules and compounds for specific diseases by analyzing huge amounts of chemical, biological and genomic data more quickly and accurately than humans. AI techniques like machine learning, deep learning, and natural language processing are being extensively used in areas like target identification, lead generation, toxicity prediction, clinical trials, and chemical compound screening during the drug development process.

The global 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.

Market Dynamics:
One of the key drivers for the growth of artificial intelligence in drug discovery market is the advancement in AI technologies like machine learning and deep learning. AI algorithms are continuously evolving, with enhanced computing power and availability of huge amounts of healthcare and biomedical data. This is allowing AI solutions to become more accurate, reliable and usable for complex tasks like predicting interactions between biological molecules during drug discovery. AI is augmenting the capabilities of scientists by automating routine tasks and helping analyze large datasets. This is significantly saving time and costs associated with new drug development. As AI continues enhancing its abilities through ongoing innovation, its applications in drug discovery are expected to grow rapidly in the long term.

SWOT Analysis
Strength: Artificial intelligence in drug discovery utilizes machine learning and deep learning algorithms to analyze large datasets which helps identify new drug candidates faster. It is also beneficial for lead optimization by allowing researchers to screen billions of compounds virtually. Moreover, AI enables drug repurposing by recognizing compounds that have potential for new therapeutic uses.

Weakness: Developing AI models that can accurately predict drug-disease interactions requires huge volumes of high-quality training data which is currently limited. There are also concerns regarding transparency and interpretability of AI algorithms used in drug discovery.

Opportunity: AI can aid in target identification, lead discovery, and clinical trial participant recruitment. With advancements in computing power and availability of genomic and healthcare databases, the role of AI in drug R&D is expected to grow significantly. AI also provides opportunities for academic-industrial collaborations to develop new drugs faster and at lower costs.

Threats: Over-reliance on data-driven drug discovery through AI could hamper innovation if biological understanding is neglected. Intellectual property issues may arise regarding ownership of AI-developed drugs and algorithms. There is also a risk of biases within AI systems affecting research outcomes or patient access to new drugs.

Key Takeaways

The Global Artificial Intelligence In Drug Discovery Market Share is expected to witness high growth over the forecast period supported by increasing investments from pharmaceutical companies and startups.

Regional analysis: North America currently dominates the market due to presence of major AI and pharmaceutical players in the US and Canada. However, Asia Pacific is expected to offer lucrative opportunities with governments launching initiatives to develop AI capabilities. China, Japan and South Korea are emerging as key hubs for AI in healthcare & drug discovery.

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.

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