SillaJen ended the session weaker, extending the previous trading day's decline, while the bio industry is seeing drug discovery methods using artificial intelligence (AI) emerge as a new source of research competitiveness.

In terms of share price movement, SillaJen finished trading at 2,360 won as of the after-market close on the 2nd, down 20 won (0.84%) from the previous trading day. The day's reference price was 2,380 won; the stock opened at 2,380 won, rose to 2,390 won, and then fell back to 2,350 won. Trading volume was tallied at 357,654 shares with a transaction value of approximately 847 million won.

On a 52-week basis, the stock has moved between a low of 1,780 won and a high of 5,400 won. The current price is above the 52-week low but still shows a considerable gap from the high. Market attention is also focused on the investment environment surrounding bio companies and the fact that drug discovery technology itself is changing rapidly.

■ AI Transforms Drug Discovery Methods

Traditional drug research required countless repeated experiments in the process of finding promising substances and verifying their efficacy and safety. Because a significant amount of time and research funding was invested from candidate discovery through actual validation, it has long been regarded as one of the highest-risk development areas in the pharmaceutical and biotech industries.

Recently, as AI has been actively applied to these research processes, changes are appearing in development methods. Whereas in the past it was common to narrow down candidate substances based on researchers' experience and accumulated experimental results, research approaches are now expanding in which AI analyzes large-scale biological and chemical data to screen promising candidate groups or suggest the next experimental direction. AI is no longer merely a supporting tool for organizing data; cases are increasing where it is used in decision-making processes in the early stages of drug discovery, such as candidate exploration, molecular structure analysis, and experimental condition setting.

■ A 'Circular Research System' Linking Data, AI, and Experiments Is the Core Competitiveness

The competitive landscape of the global bio industry is also changing. Recently, rather than simply securing a single high-performance AI algorithm, building a system that connects AI analysis results with actual laboratory experimental processes is becoming increasingly important.

A circular research system is emerging as a key competitive factor: when AI proposes a specific candidate, the laboratory validates it, and those results are accumulated as data that is then reflected in the AI model's next judgment. In effect, the center of gravity in drug discovery competition is shifting from simply securing candidate substances toward connecting data, AI, and experimental infrastructure into a single research process.

■ 'AI Use ≠ Guaranteed Success'... Development Risks Remain

However, the use of AI technology does not immediately improve the likelihood of drug discovery success. While AI can be a means of improving efficiency at the research stage, the actual drug development process—including clinical trials and regulatory review—still requires considerable time and cost.

It is worth noting that the share prices of domestic bio stocks, including SillaJen, may see expanded volatility depending on the clinical progress of individual pipelines, R&D results, financing conditions, and market investment sentiment.

[Investment decisions and the responsibility for them rest with the investor. AI assistance was used in writing and composing this article.]