Artificial intelligence (AI) is moving beyond being a mere tool for identifying drug candidates and is now deeply integrated into the entire drug development process. Its applications are rapidly expanding, covering everything from disease target discovery to protein structure prediction, candidate design and optimization, toxicity prediction, clinical trial participant selection, and forecasting clinical success. In response, major global pharmaceutical companies are ramping up their investments in AI.
According to reports from Reuters and other outlets on September 20, the scope of AI collaborations among leading pharmaceutical companies is broadening beyond individual candidate discovery to encompass research and development, manufacturing, and commercialization.
Eli Lilly announced in January that it would invest up to $1 billion over five years in a drug development lab that combines AI and robotics in partnership with NVIDIA. The plan involves using NVIDIA's latest AI chips to design drug candidates while simultaneously generating the research data needed for AI model training.
In June, Novartis entered into a collaboration with U.S. biotech company Orionis Biosciences to develop molecular glue drugs. The agreement includes an upfront payment of $40 million, with potential milestones bringing the total to $1.4 billion. The goal is to leverage Orionis's AI-based discovery platform to target previously difficult-to-address drug targets using molecular glue technology. This marks the second collaboration between the two companies, following their initial agreement in 2020.
A notable example of AI's involvement in the entire drug development process is the candidate drug 'Rentosertib' for idiopathic pulmonary fibrosis (IPF), developed by Hong Kong-based Insilico Medicine. The drug was selected as a clinical candidate within 18 months after AI identified a new target, TNIK, and designed the drug molecule using the generative AI platform 'Chemistry 42.' It is currently in Phase 3 clinical trials in China.
On September 7, the journal Nature Biotechnology published research indicating that patients treated with Rentosertib showed biological age reversal across six independently developed proteomic aging clock indicators. This marks the first instance of a drug candidate identified by AI demonstrating potential for age reversal in clinical settings, drawing significant attention from the industry.
As the use of AI in the biotech sector rapidly expands, the market is expected to experience significant growth. According to global market research firm Mordor Intelligence, the AI market in the pharmaceutical and biotech sectors is projected to grow from $6.16 billion this year to $34.99 billion by 2031, representing an annual growth rate of approximately 41%.
Kim Min-seok, a senior researcher at the Korea Health Industry Development Institute, stated, "AI technology has emerged as a key means to drive innovation throughout the entire drug development cycle. It is increasingly being utilized in areas such as optimizing clinical trial design, developing patient-specific therapies, and repurposing drugs, serving as a central axis for transforming existing paradigms across corporate operations."
The market outlook suggests that competition among pharmaceutical companies will hinge on the quality of research and clinical data they can secure and how quickly they can validate AI-generated predictions through experimentation. The ability to effectively connect automated research facilities with clinical development organizations is also seen as a critical competitive advantage. Companies that can translate AI-derived research results into actual drug development and commercialization are expected to lead in the competitive landscape.
According to reports from Reuters and other outlets on September 20, the scope of AI collaborations among leading pharmaceutical companies is broadening beyond individual candidate discovery to encompass research and development, manufacturing, and commercialization.
Eli Lilly announced in January that it would invest up to $1 billion over five years in a drug development lab that combines AI and robotics in partnership with NVIDIA. The plan involves using NVIDIA's latest AI chips to design drug candidates while simultaneously generating the research data needed for AI model training.
In June, Novartis entered into a collaboration with U.S. biotech company Orionis Biosciences to develop molecular glue drugs. The agreement includes an upfront payment of $40 million, with potential milestones bringing the total to $1.4 billion. The goal is to leverage Orionis's AI-based discovery platform to target previously difficult-to-address drug targets using molecular glue technology. This marks the second collaboration between the two companies, following their initial agreement in 2020.
A notable example of AI's involvement in the entire drug development process is the candidate drug 'Rentosertib' for idiopathic pulmonary fibrosis (IPF), developed by Hong Kong-based Insilico Medicine. The drug was selected as a clinical candidate within 18 months after AI identified a new target, TNIK, and designed the drug molecule using the generative AI platform 'Chemistry 42.' It is currently in Phase 3 clinical trials in China.
On September 7, the journal Nature Biotechnology published research indicating that patients treated with Rentosertib showed biological age reversal across six independently developed proteomic aging clock indicators. This marks the first instance of a drug candidate identified by AI demonstrating potential for age reversal in clinical settings, drawing significant attention from the industry.
As the use of AI in the biotech sector rapidly expands, the market is expected to experience significant growth. According to global market research firm Mordor Intelligence, the AI market in the pharmaceutical and biotech sectors is projected to grow from $6.16 billion this year to $34.99 billion by 2031, representing an annual growth rate of approximately 41%.
Kim Min-seok, a senior researcher at the Korea Health Industry Development Institute, stated, "AI technology has emerged as a key means to drive innovation throughout the entire drug development cycle. It is increasingly being utilized in areas such as optimizing clinical trial design, developing patient-specific therapies, and repurposing drugs, serving as a central axis for transforming existing paradigms across corporate operations."
The market outlook suggests that competition among pharmaceutical companies will hinge on the quality of research and clinical data they can secure and how quickly they can validate AI-generated predictions through experimentation. The ability to effectively connect automated research facilities with clinical development organizations is also seen as a critical competitive advantage. Companies that can translate AI-derived research results into actual drug development and commercialization are expected to lead in the competitive landscape.
* This article has been translated by AI.
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