Anindya Bagchi is an Associate Professor at the Sanford Burnham Prebys Medical Discovery Institute and an expert in cancer genetics. He is also the founder of several biotech companies.
Dear Anindya,
What could be achieved if there was a public or nonprofit AI effort with the same scale and level of funding as the current large private efforts? What would be the benefits for society?
Anindya:
The absence of a large-scale nonprofit or publicly available AI platform is a significant issue, especially given the vast amount of scientific data now accessible in the public domain. It is crucial to have such a resource, as research funded by public money generates data that should benefit the public as quickly and efficiently as possible. Relying solely on privately funded AI tools to process and extract insights from this data is problematic. With the massive volume of data available, we need to do a better job of distinguishing between noise and meaningful information—an area where AI excels, by identifying patterns and underlying structures that would otherwise take years to uncover.
For instance, my own work focuses on cancer, and I have a personal connection to the disease—recently, a friend of mine passed away from glioblastoma. In the past few decades, we've only managed to extend survival in glioblastoma patients by a few months. AI has the potential to accelerate progress by cutting through the noise and revealing the critical insights needed to drive significant advancements. While we’ve seen impressive progress in the private sector, we cannot solely depend on private enterprises to sift through and prioritize high-impact data. There is an unmet need in the public research space that is not being addressed quickly enough.
In my research, I’ve used AlphaFold 3, an AI tool developed by a private company, and I found it to be incredibly valuable. For example, I used AlphaFold 3 to model a protein-protein interaction between a novel microprotein and RAS, one of the key oncogenes in cancer. Remarkably, AlphaFold 3 predicted the structure with great accuracy, which we confirmed experimentally. This saved us years of work by allowing us to zero in on the crucial details right away. My collaborator, Jianhua Zhao, and I are following up with cryo-EM experiments, but we already have substantial insights thanks to the AI predictions. While I’ve tried other software, nothing has come close to the precision of AlphaFold 3. Genomic and molecular data can indicate which genes are amplified, deleted, or mutated, but understanding the precise mechanisms requires structural information—this is where AI can make a transformative difference.
I’ve also used AI systems like ChatGPT and Gemini to explore scientific fields that are relatively new to me. While these tools have their limitations—such as providing exact references—they serve as useful introductions, prompting further investigation into original papers.
If we had a publicly accessible, large-scale AI platform, agencies like the NIH could build on it, much like they did with the creation of the National Library of Medicine and PubMed. Just as countless innovations have been built on open resources like the GPS system and the Internet, a public AI platform could spur further advancements and significantly accelerate scientific discovery.
In summary, a publicly available AI system would not only democratize access to powerful tools but also hasten the translation of scientific discoveries into meaningful outcomes. The potential benefits to research, patient outcomes, and public health are too great to ignore.
We are encouraging researchers at different career stages to share ideas about complex science problems that could benefit from a large-scale AI effort. We found that motivation and recognition could be provided if you and other well-known scientists were willing to talk to people that suggest the best ideas. You would be the judge and decide if any idea is for you deserving of attention. Any scientist selected might receive advice but could also be a potential collaborator. Many ideas will be produced, and society will take notice. Would you be willing to talk to any of these scientists?
Anindya:
Yes, I particularly enjoy one-on-one scientific interactions and collaborations. There are many forms of collaboration—some are simply about dividing tasks. But the kind I prefer is more like the Francis Crick model. Crick shared his office with several prominent scientists (for example, Jim Watson, Sidney Brenner among several others), and they would spend hours discussing all aspects of their research. These discussions led to deeper insights, sparking new ideas and directions, and real breakthroughs.
AI can play a unique role in this collaborative process. It could analyze your data, identify the missing pieces, and even suggest potential collaborators who could fill in those gaps. In that way, AI becomes not just a tool, but a facilitator of more productive scientific partnerships.
As human scientists, we often carry certain biases—whether it’s in our focus, our methods, or the fields we believe are most important. AI can offer a counterbalance because its limitations are different from ours. For example, I’m a geneticist, and for a long time, I believed my field was the key to understanding everything. But over time, I’ve come to realize how essential structural biology is. I wish I had recognized this sooner.
We also tend to be distracted by short-term objectives, like securing the next grant or publishing the next paper. This can lead to a mentality where we keep "milking the same cow," following safe, familiar paths without truly advancing toward solving the bigger problems we’re working on—like curing cancer or significantly improving patient outcomes. AI may not solve these problems by itself, but it can help cut through the noise and refocus our efforts on what really matters.
It's easy to get fixated on the molecule we've studied for years or our favorite hypothesis, even when the evidence starts to point in other directions. I’ve also seen a lot of groupthink in my career, where a new trend or fashionable theory sweeps the field, and everyone follows it because it’s easier to publish and get funding. I’ve seen many such trends come and go, leaving little impact behind.
We should aim to make meaningful contributions, even if only a few of us will achieve them. And just as importantly, we need help failing faster when we’re on the wrong path. AI can play a key role in that—helping us avoid unproductive avenues and accelerating progress toward solutions.
We are at a pivotal moment in the history of civilization. AI has the potential to help us understand and solve major problems faster than ever before. By accelerating scientific discovery, we can contribute more to society. In turn, society will provide the resources we need to continue this work. This, I believe, is the real solution to the problem of excessive competition with very little to show for, which many have pointed out as a barrier to effective scientific collaboration.