Pharmaceutical giant Lilly recently announced a partnership with OpenAI to utilize generative AI for developing new antibiotics against drug-resistant bacteria. (Link: https://investor.lilly.com/news-releases/news-release-details/lilly-collaborates-openai-discover-novel-medicines-treat-drug) This collaboration underscores the growing recognition of AI's potential in tackling major healthcare issues, even in areas with limited financial returns.
The rise of drug-resistant bacteria is a serious global health threat. These superbugs are rendering existing antibiotics ineffective, making infections more challenging and expensive to treat. The development of new antibiotics is vital in this fight.
While the press release doesn't provide specific details, it's likely that scientists from both companies will leverage the concepts discussed in my previous article (https://world.hey.com/hanjo/generative-ai-in-drug-discovery-a-human-in-the-loop-perspective-2c59a5d8) to explore various experimental approaches. Their unique perspectives and expertise will shape the direction of these experiments, and the results will become evident over time.
The Lilly-OpenAI partnership exemplifies how pharmaceutical companies can leverage AI beyond their core business areas. By exploring AI in these non-core areas, companies can gain valuable insights and develop innovative approaches applicable to their core businesses in the future.
Collaborating in non-core areas and gradually applying the results to core areas is a rational approach being considered by many pharmaceutical companies. This approach stems from the challenges faced by large organizations in minimizing resistance from legacy systems while improving processes. Transferring success from one area to another is a significant challenge, but success stories always open minds. The openness of experts within legacy systems is proportional to their tolerance for trial and error. The fact that AI performance improves with increasing data volume is one factor that can extend this period of tolerance.
AI drug discovery startups sell a vision of an improved (or a disruptive) process. To sell this vision, they need to be backed by strong success stories. These stories will inevitably lead to changes in the organization's structure and work style when they are adopted by other organizations. As a result, researchers within these organizations will also be asked to change. Convincing team members to embrace this change naturally is one of the most critical points for leaders within organizations that want to adopt AI drug discovery technology.
Key Takeaways:
- AI is a powerful tool for addressing critical healthcare challenges, even in areas with limited financial incentives.
- Pharmaceutical companies can leverage AI in non-core areas to gain valuable insights and drive innovation.
- Success stories of AI applications will encourage broader exploration and adoption across the pharmaceutical industry.
- Overcoming resistance to change is crucial for successful AI adoption in drug discovery.
The rise of drug-resistant bacteria is a serious global health threat. These superbugs are rendering existing antibiotics ineffective, making infections more challenging and expensive to treat. The development of new antibiotics is vital in this fight.
While the press release doesn't provide specific details, it's likely that scientists from both companies will leverage the concepts discussed in my previous article (https://world.hey.com/hanjo/generative-ai-in-drug-discovery-a-human-in-the-loop-perspective-2c59a5d8) to explore various experimental approaches. Their unique perspectives and expertise will shape the direction of these experiments, and the results will become evident over time.
The Lilly-OpenAI partnership exemplifies how pharmaceutical companies can leverage AI beyond their core business areas. By exploring AI in these non-core areas, companies can gain valuable insights and develop innovative approaches applicable to their core businesses in the future.
Collaborating in non-core areas and gradually applying the results to core areas is a rational approach being considered by many pharmaceutical companies. This approach stems from the challenges faced by large organizations in minimizing resistance from legacy systems while improving processes. Transferring success from one area to another is a significant challenge, but success stories always open minds. The openness of experts within legacy systems is proportional to their tolerance for trial and error. The fact that AI performance improves with increasing data volume is one factor that can extend this period of tolerance.
AI drug discovery startups sell a vision of an improved (or a disruptive) process. To sell this vision, they need to be backed by strong success stories. These stories will inevitably lead to changes in the organization's structure and work style when they are adopted by other organizations. As a result, researchers within these organizations will also be asked to change. Convincing team members to embrace this change naturally is one of the most critical points for leaders within organizations that want to adopt AI drug discovery technology.
Key Takeaways:
- AI is a powerful tool for addressing critical healthcare challenges, even in areas with limited financial incentives.
- Pharmaceutical companies can leverage AI in non-core areas to gain valuable insights and drive innovation.
- Success stories of AI applications will encourage broader exploration and adoption across the pharmaceutical industry.
- Overcoming resistance to change is crucial for successful AI adoption in drug discovery.