The South Korean pharmaceutical and biotech industry is eager to adopt artificial intelligence (AI), but a shortage of specialized personnel has emerged as a significant challenge to the expansion of AI in drug development. Approximately 90% of surveyed companies are either utilizing AI or considering its implementation, yet 67.3% reported a lack of AI specialists.
The Korea Pharmaceutical and Bio-Pharma Manufacturers Association's AI Drug Research Institute conducted a survey to assess awareness and training needs regarding AI in drug development. The survey involved 44 domestic pharmaceutical and biotech companies, AI firms, academic institutions, and research organizations, with responses from 55 individuals, and the results were announced on July 31.
Among the 55 respondents, 26 indicated they are currently using AI in drug development. The methods of utilization included internal use by 10 respondents, outsourcing by 9, and a combination of both by 7. Additionally, 23 respondents are considering or planning to implement AI, while 6 reported no plans for utilization.
Respondents using AI identified the most common applications as candidate substance identification and optimization, each cited by 15 individuals. Other areas included compound identification (11), mechanism of action (9), target identification and validation (8), preclinical (6), clinical (4), and drug repurposing (4).
Looking ahead, the most desired area for development is synthetic drugs, with 27 respondents (49.1%) indicating interest. This was followed by biopharmaceuticals and antibodies (12 respondents, 21.8%), targeted protein degradation (6, 10.9%), antibody-drug conjugates (5, 9.1%), and drug delivery systems (3, 5.5%).
However, the shortage of AI specialists remains a pressing issue. A total of 37 respondents (67.3%) reported having no AI specialists in their drug development departments. The breakdown of personnel included 10 respondents (18.2%) with four or more specialists, 4 (7.3%) with one, 3 (5.5%) with two, and 1 (1.7%) with three.
Reasons for the lack of AI specialists included insufficient funding and resources for recruitment (26 respondents), a lack of understanding of AI technology among existing staff (19), and a lack of awareness regarding the necessity of AI in drug development (11).
When asked about the support they seek from the government and related organizations, 37 respondents prioritized training for AI specialists. Other requests included the establishment of data-sharing platforms (25), investment and funding support (23), opportunities for validation and verification (22), guidelines for regulatory approval (15), and support for global expansion (9).
The need for AI education is also significant, with 91.0% (50 respondents) affirming the necessity of training in AI drug development. Specific training needs included AI algorithms and modeling programming (17), data analysis and visualization (16), bioinformatics and cheminformatics data utilization (13), and software and programming skills (5).
Understanding of autonomous laboratories (SDL) remains low, with 38% (21 respondents) having only heard the term and 36% (20) unaware of it entirely. Only 24% (13) claimed to understand the concept. SDL refers to a research system that combines AI with automated laboratory robots to automate the entire process from experimental design to execution, data analysis, and follow-up experiments.
Yoon Yeon-hong, president of the Korea Pharmaceutical and Bio-Pharma Manufacturers Association, stated, "AI has become an essential capability in drug development, but domestic companies face the practical limitation of a shortage of specialized personnel. If the industry and government systematically support the training of AI specialists, the establishment of data infrastructure, and the creation of validation environments, South Korea's competitiveness in AI drug development could significantly improve."
Meanwhile, drug development is recognized as a high-risk, high-cost industry, typically requiring an average of 10 to 15 years and substantial financial investment, with a clinical failure rate of 90%. Given that a significant portion of drug development costs occurs in late-stage clinical trials, efforts are ongoing to identify candidate substances with a higher likelihood of success in the early stages and to reduce trial and error.
* This article has been translated by AI.
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