Today, we have the privilege of speaking with Ivan Kairatov, a biopharma expert at the forefront of technological innovation in research and development. His work delves into one of the most exciting intersections in modern medicine: the use of artificial intelligence to accelerate the fight against cancer. We’ll be exploring a groundbreaking study that harnessed sophisticated machine learning to identify new potential cancer drugs. Our conversation will touch on how these intelligent algorithms sift through millions of possibilities to find promising candidates, the critical journey these digital discoveries take from the computer to the lab bench, and the profound impact this technology has on the cost and speed of developing new therapies. We’ll also discuss the delicate and essential partnership between AI’s predictive power and irreplaceable human expertise.
Your research used AI to perform a large-scale virtual screening that identified 14 candidate CDK9 inhibitors. Could you walk us through how these machine learning algorithms work and what key molecular characteristics they prioritize to make their selections?
Of course. Think of it less like a simple checklist and more like teaching a computer to develop an intuition. We feed these machine learning models enormous amounts of data on existing molecules—what they look like in three dimensions, their chemical properties, and, most importantly, how strongly they bind to specific proteins like CDK9. The AI learns to recognize incredibly complex patterns that we might miss, patterns that define a successful inhibitor. For CDK9, the algorithms were prioritizing molecules that could form a stable, high-affinity bond within the protein’s active site. It wasn’t just about size or shape; it was about predicting the intricate dance of atomic interactions, the hydrogen bonds, and the hydrophobic forces that would effectively shut the enzyme down. This large-scale virtual screening allowed us to rapidly filter an immense chemical library down to just 14 candidates that the AI flagged as having the highest probability of success.
After testing the 14 candidates, two compounds showed significant cytotoxic activity in cervical and breast cancer cell models. What specific properties made these two molecules stand out during experimental validation, and what are the next steps for developing them into potential therapies?
It’s always a thrilling moment when a computational prediction holds up in the real world. When we moved those 14 candidates into cellular models, two of them truly distinguished themselves. They didn’t just bind to the protein; they effectively crippled the cancer cells’ ability to survive and replicate. What made them stand out was their potent cytotoxic activity at low concentrations, meaning they were highly efficient killers of cervical and breast cancer cells. This suggests their molecular structure was almost perfectly tailored to inhibit CDK9 in a live cellular environment, leading to a cascade of events that halted cell proliferation. The next steps are multifaceted. We need to optimize these compounds to improve their drug-like properties—making them safer, more stable, and more easily absorbed by the body. This involves a new Drug Discovery project, where we’ll explore both natural and synthetic variations, again using AI, to build upon the very encouraging results we’ve already seen.
The CDK9 protein is a key therapeutic target due to its role in gene transcription and cell proliferation. Could you explain why inhibiting this specific protein is so effective against tumor growth and how this targeted approach differs from more conventional cancer treatments?
The reason CDK9 is such a compelling target goes back to the foundational work of Professor Antonio Giordano, who first characterized it. This enzyme acts as a master regulator for gene transcription. In essence, it’s one of the key gatekeepers that allows a cell to read its DNA and produce the proteins it needs to grow and divide. In many cancers, CDK9 goes into overdrive—its hyperactivation is what fuels the uncontrolled proliferation that defines a tumor. By specifically inhibiting CDK9, we are cutting off the fuel supply at the source. This is fundamentally different from conventional chemotherapies, which are often blunt instruments that kill any rapidly dividing cell, leading to severe side effects. A targeted CDK9 inhibitor is more like a guided missile; it’s designed to shut down a process that cancer cells are uniquely dependent on, which we hope will lead to more effective and less toxic treatments.
The initial drug discovery phase can be very time-consuming and costly. Can you share any specific metrics or examples that illustrate how this AI-driven approach concretely reduced the project’s timeline or expenses compared to traditional screening methods?
While I can’t give you exact dollar amounts, the difference is night and day. Traditionally, identifying a handful of promising leads would involve physically screening hundreds of thousands, if not millions, of compounds in a high-throughput lab. This requires immense resources: chemical reagents, robotic equipment, and countless hours of work. The process can take years. With our machine learning-based virtual screening, we compressed that initial phase dramatically. We computationally evaluated a vast chemical space and narrowed it down to just 14 highly probable candidates for physical testing. As Professor Tiziano Tuccinardi noted, this approach makes it possible to drastically reduce the time and costs. It’s not just about saving money; it’s about increasing the probability of success from the outset, so we’re not wasting resources testing compounds that were never going to work in the first place.
While AI offers immense predictive power, human experimental validation remains essential. Could you describe the process of moving from a promising computational result to the physical lab work and share an instance where human expertise was critical to interpreting the data correctly?
This is such a crucial point. The AI gives us a highly educated guess—a beautifully rendered map, if you will. But we, the human researchers, still have to make the journey. Once the AI identified the 14 candidates, our lab teams had to synthesize or procure these molecules and then design the cellular assays. We grow cultures of cervical and breast cancer cells and expose them to the compounds, meticulously measuring their viability and signs of cell death. I recall a moment with one of the compounds where the initial data was ambiguous. The cell death rates weren’t as clean as we’d hoped. An algorithm might have just classified it as a moderate success. But an experienced biologist on our team noticed a subtle morphological change in the cells under the microscope before they died, suggesting a very specific cellular pathway was being triggered. That human observation, that irreplaceable gut feeling born from years in the lab, guided our next set of experiments and helped us understand the molecule’s mechanism in a way the raw numbers never could have.
What is your forecast for the integration of AI in oncological drug discovery over the next decade?
I believe we are on the cusp of a true paradigm shift. Over the next decade, AI will become a fully integrated, indispensable partner in every stage of drug discovery, from identifying novel targets to designing clinical trials. We will see AI models that can not only predict a molecule’s efficacy but also its potential toxicity and side effects before it’s ever synthesized, saving countless resources. Furthermore, AI will enable truly personalized oncology, designing bespoke molecules or drug cocktails tailored to a specific patient’s tumor genetics. However, the future isn’t a world run by algorithms; it’s one of synergy. The most profound breakthroughs will come from the seamless integration of AI’s massive analytical power with the creativity, intuition, and critical judgment of human scientists. The future of fighting cancer is a collaborative one.
