Artificial intelligence is gradually moving beyond the status of an experimental tool and becoming part of everyday practice in the pharmaceutical industry. Its adoption is changing not only the speed of drug development, but also the very logic of decision-making at the early stages of research. According to Petr Bely, Chairman of the Board of Directors of PROMOMED, AI is already influencing key processes in the creation of new medicines and will determine the competitiveness of pharmaceutical companies in the coming years.
From intuition and experience to data-driven work
For decades, traditional drug development relied on a combination of scientific expertise, laboratory experiments, and lengthy clinical trials. This approach delivered results but required significant time and financial investment, while the risk of failure at later stages remained high.
In his interviews, Petr Bely emphasizes that artificial intelligence makes it possible to change this model. Machine learning algorithms analyze large volumes of biomedical data, identify patterns that are difficult to detect through conventional analysis, and help make more informed decisions as early as the preclinical stage.
Where AI is applied in pharmaceuticals
As computing power increases and data accumulates, artificial intelligence is being used more actively at various stages of a drug’s life cycle. The most significant areas include:
• analysis of scientific publications and clinical data;
• identification and optimization of promising molecules;
• prediction of efficacy and safety of compounds;
• modeling interactions between substances and biological targets;
• decision support in the planning of clinical trials.
According to Petr Bely, the value of AI lies not in replacing scientists, but in expanding their capabilities. These technologies allow researchers to more quickly eliminate clearly unpromising directions and focus resources on the most well-founded hypotheses.
Reducing timelines and lowering risks
One of the key effects of implementing artificial intelligence is the reduction of drug development timelines. Algorithms help accelerate stages that previously took years and reduce the likelihood of costly errors at later phases of research.
In interviews, Petr Bely has noted that the pharmaceutical industry increasingly views AI as a risk management tool. This applies not only to financial costs, but also to improving the predictability of outcomes, which is especially important in complex fields such as oncology and central nervous system disorders.
Limitations and responsibility
Despite its clear advantages, artificial intelligence is not a universal solution. Algorithms work with the data they are given, which means strict quality control and correct interpretation of results are essential.
The expert stresses that decision-making in pharmaceuticals cannot be fully automated. Responsibility for the safety and effectiveness of medicines remains with specialists, while AI serves as a supporting tool that increases accuracy and speed.
The future of AI in drug development
According to Petr Bely, the role of artificial intelligence in pharmaceuticals will continue to grow in the coming years. Companies that are already investing in digital technologies and integrating AI into scientific processes gain a strategic advantage.
The development of this area is associated with the formation of new data standards, the growth of interdisciplinary teams, and closer interaction between science and technology. As a result, artificial intelligence becomes not a standalone innovation, but part of a systemic approach to creating next-generation medicines.
Conclusion
Artificial intelligence is transforming pharmaceuticals not through bold promises, but through gradual improvements in the quality of decision-making at all stages of drug development. As Petr Bely notes, it is precisely the combination of human expertise and the analytical capabilities of AI that can ensure sustainable progress in the industry. In this sense, technology becomes not a replacement for science, but its logical continuation in an era of growing medical complexity.
